U.S. patent application number 16/396575 was filed with the patent office on 2019-10-31 for detecting, prioritizing, and validating unpermitted renovation events through data mining, natural language processing, and mach.
The applicant listed for this patent is Deckard Technologies, Inc.. Invention is credited to Neil DUTTA, Gregory ROSE, Neil SENTURIA.
Application Number | 20190332961 16/396575 |
Document ID | / |
Family ID | 68291207 |
Filed Date | 2019-10-31 |
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United States Patent
Application |
20190332961 |
Kind Code |
A1 |
ROSE; Gregory ; et
al. |
October 31, 2019 |
DETECTING, PRIORITIZING, AND VALIDATING UNPERMITTED RENOVATION
EVENTS THROUGH DATA MINING, NATURAL LANGUAGE PROCESSING, AND
MACHINE LEARNING
Abstract
Described are media, systems, and methods to detect an
unpermitted renovation event and validate the detected event from a
data set ingested from a plurality of unique external data sources
by identifying candidate renovations, determining the probability
and timing of an unpermitted renovation associated with the
candidate renovation, and validating the probability and timing of
the unpermitted renovation. Further described are applications and
methods to prioritize inspection of unpermitted renovation
candidates and validate the prioritization.
Inventors: |
ROSE; Gregory; (San Diego,
CA) ; SENTURIA; Neil; (San Diego, CA) ; DUTTA;
Neil; (La Jolla, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Deckard Technologies, Inc. |
La Jolla |
CA |
US |
|
|
Family ID: |
68291207 |
Appl. No.: |
16/396575 |
Filed: |
April 26, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62748991 |
Oct 22, 2018 |
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62718751 |
Aug 14, 2018 |
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62695564 |
Jul 9, 2018 |
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62671957 |
May 15, 2018 |
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62664591 |
Apr 30, 2018 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 50/26 20130101;
H04L 67/10 20130101; G06N 5/048 20130101; G06N 7/005 20130101; G06N
20/00 20190101; G06Q 50/163 20130101; G06F 40/30 20200101; G06Q
50/16 20130101; G06F 40/268 20200101; H04L 67/1097 20130101; G06F
16/2465 20190101; G06N 5/022 20130101; G06F 16/29 20190101; H04L
67/02 20130101; G06N 3/08 20130101; G06N 5/003 20130101; G06N
3/0445 20130101; H04L 67/125 20130101; G06F 40/279 20200101; G06N
5/041 20130101; G06N 5/046 20130101; G06F 2216/03 20130101; G06N
20/10 20190101; G06N 20/20 20190101; H04L 67/04 20130101 |
International
Class: |
G06N 5/04 20060101
G06N005/04; G06N 20/00 20060101 G06N020/00; G06F 16/2458 20060101
G06F016/2458; G06F 17/27 20060101 G06F017/27 |
Claims
1. A non-transitory computer-readable storage media encoded with a
computer program including instructions executable by a processor
to create an application to detect an unpermitted renovation event
and validate the detected event, the application comprising: a) a
parameter setting module that defines a data set to be evaluated;
b) a plurality of data ingestion interfaces, each interface
connecting to a unique external data source, each interface
configured to perform a data mining task process to its data source
to detect one or more unpermitted renovation event indicia within
the data set; c) a renovation detection module that applies a
machine learning algorithm to identify an initial candidate based
on the detection indicia within the data set; d) a renovation
probability calculation module that calculates a probability that
an unpermitted renovation event has taken or is taking place at the
initial candidate; and e) a validation module that accepts verified
data regarding the unpermitted renovation event and feeds back the
verified data to the renovation probability calculation module to
improve its prediction over time.
2. The media of claim 1, wherein the data set is defined by at
least one of a street address, a parcel, a street, a lot, a zip
code, a county, a state, an area drawn on a map, an area within a
set radial distance from a location, coordinates set by one or more
satellites, an area within a set driving distance of a location, a
GPS point, or an area defined by at least three GPS points.
3. The media of claim 1, wherein the data mining task process
comprises a natural language process, numerical data mining
process, or a photographic data mining task process.
4. The media of claim 3, wherein the natural language task process
comprises syntax interpretation, semantic interpretation, discourse
interpretation, or speech interpretation.
5. The media of claim 4, wherein the syntax interpretation
comprises lemmatization, morphological segmentation, part-of-speech
tagging, parsing, sentence boundary disambiguation, stemming, word
segmentation, or terminology extraction.
6. The media of claim 4, wherein the semantic interpretation
comprises lexical semantics, machine translation, named entity
recognition, natural language generation, natural language
understanding, optical character recognition, question answering,
recognizing textual entailment, relationship extraction, sentiment
analysis, topic segmentation, RNN language modeling, Deep Learning,
or word sense disambiguation.
7. The media of claim 4, wherein the discourse interpretation
comprises automatic summarization, coreference resolution, or
discourse analysis.
8. The media of claim 4, wherein the speech interpretation
comprises speech recognition, speech segmentation, and
text-to-speech.
9. The media of claim 1, wherein the external data source comprises
city property records, county property records, city permit
records, county permit records, post office address database, state
business records, historical real estate listings, rental listings,
demolition orders, dumpster orders, portable restroom orders,
customer account information from third party companies, social
media, phone records, address records, historical credit card
history purchase records, satellite images, tax records, street
views, online photographs, online videos, signs outside a property,
aircraft photos, parking infringements, noise complaints, landfill
records, traffic permits, or the Internet.
10. The media of claim 1, wherein the detection of one or more
unpermitted renovation event indicia comprises determining a square
footage of a property, a change in the square footage of a
property, a bed count of a property, a change in a bed count of a
property, a bathroom count of a property, a change in a bathroom
count of a property, a change in a parking count of a property, a
change in a garage size of a property, a valuation of a property, a
change in a valuation of the property, ownership of a property, a
corporation owning a property, an owner with a history of flipping
one or more properties, an owner with a history of unpermitted
renovations one or more properties, lenders on a property, a change
in the rent value of the property, a discrepancy between a current
and a prior rental listing, or a lien on a property.
11. The media of claim 1, wherein the calculation comprises
applying an increased weighted factor that the unpermitted
renovation event has taken place, providing an indicator for a
machine learning algorithm, or both if (a) a property is owned by a
corporation, (b) one or more corporate officers has previously
flipped properties, (c) a property owner's social media displays
renovations, (d) a real estate listing displays renovations, or (e)
any combination thereof.
12. The media of claim 1, wherein the calculation comprises
calculating whether a probability threshold has been met.
13. The media of claim 1, wherein the verified data is acquired by
a public official inspecting a candidate property.
14. The media of claim 1, wherein the verified data is an issued
permit for the renovated event at the initial candidate.
15. The media of claim 1, further comprising a secondary screening
module, wherein if the probability calculation module calculates a
probability in excess of a predetermined threshold, the secondary
screening module proceeds to conduct further screening
procedures.
16. The media of claim 1, wherein the validation module further
provides at least one of the probability and the initial candidate
to an inspector.
17. The media of claim 1, wherein the renovation detection module
identifies a plurality of unpermitted renovation candidates based
on the detection indicia within the data set.
18. The media of claim 17, wherein the application further
comprises: a) an active renovation probability calculation module
that calculates a probability that each unpermitted renovation
event is an active renovation event; and b) a prioritization
validation module that accepts verified data regarding the
unpermitted renovation event, active renovation event, or time
until the active renovation event is complete and feeds back the
verified data to the renovation detection calculation module and
active renovation probability calculation module to improve their
prediction over time.
19. The media of claim 18, further comprising an active renovation
completion estimator module that assigns a value estimating the
time until the active renovation event is completed.
20. The media of claim 18, further comprising an unpermitted
renovation candidate to inspector location distance calculation
module that calculates a distance between the plurality of
unpermitted renovation candidates and a location of an
inspector.
21. The media of claim 18, further comprising an inspection
prioritization module that provides an order for the inspector to
prioritize inspecting the plurality of unpermitted renovation
candidates based on the probability that the unpermitted renovation
event is an active renovation event, the estimated value regarding
the time until the active renovation event is completed, and the
distance between the plurality of unpermitted renovation candidates
and the location of the inspector.
22. The media of claim 18, wherein the active renovation
probability calculation comprises applying an increased weighted
factor that an active renovation event has taken place if (a) a
property is owned by a corporation, (b) one or more corporate
officers has previously flipped properties, (c) a property owner's
social media displays renovations, (d) a real estate listing
displays renovations, (e) if an unpermitted renovation candidate
was acquired more recently, or (f) any combination thereof.
23. The media of claim 18, wherein the prioritization comprises
applying an increased priority factor as (a) the value estimating
the time until the active renovation event is completed approaches
zero, (b) the calculated distance between the plurality of
unpermitted renovation candidates and a location of an inspector
approaches zero, or (c) any combination thereof.
24. The media of claim 21, wherein the prioritization validation
module accepts verified data regarding the value of the unpermitted
renovation event and feeds back the verified data regarding the
value of the unpermitted renovation event to the renovation value
calculation module to improve its prediction over time.
25. A computer implemented system comprising: a computer-readable
storage device coupled to at least one processor and having
instructions stored thereon which, when executed by the at least
one processor, causes the at least one processor to perform
operations comprising: a) defining, by a parameter setting module,
a data set to be evaluated; b) detecting, by a plurality of data
ingestion interfaces, one or more unpermitted renovation event
indicia within the data set, wherein each interface connects to a
unique external data source, and wherein each interface performs a
data mining task process to its data source to detect the one or
more unpermitted renovation event indicia; c) identifying, by a
renovation detection module, an initial candidate by applying a
machine learning algorithm to unpermitted renovation event indicia
within the data set d) calculating, by a renovation probability
calculation module, a probability that the an unpermitted
renovation event has taken or is taking place at the initial
candidate e) accepting, by a validation module, verified data
regarding the unpermitted renovation event; and f) feeding back the
verified data to the renovation probability calculation module to
improve its prediction over time.
26. The system of claim 1, further identifying, by the renovation
detection module, a plurality of unpermitted renovation candidates
based on the detection indicia within the data set.
27. The system of claim 26 further comprising: a) calculating, by
an active renovation probability calculation module, a probability
that each unpermitted renovation event is an active renovation
event; b) accepting, by a prioritization validation module,
verified data regarding the unpermitted renovation event, active
renovation event, or time until the active renovation event is
complete; and c) feeding back the verified data from b) to the
renovation detection calculation module and active renovation
probability calculation module to improve their prediction over
time.
28. A computer-implemented method for to detecting an unpermitted
renovation event and validating the detected event comprising: a)
defining, by a parameter setting module, a data set to be
evaluated; b) detecting, by a plurality of data ingestion
interfaces, one or more unpermitted renovation event indicia within
the data set, wherein each interface connects to a unique external
data source, and wherein each interface performs a data mining task
process to its data source to detect the one or more unpermitted
renovation event indicia; c) identifying, by a renovation detection
module, an initial candidate by applying a machine learning
algorithm to unpermitted renovation event indicia within the data
set d) calculating, by a renovation probability calculation module,
a probability that the an unpermitted renovation event has taken or
is taking place at the initial candidate e) accepting, by a
validation module, verified data regarding the unpermitted
renovation event; and f) feeding back the verified data to the
renovation probability calculation module to improve its prediction
over time. The method of claim 1, further identifying, from the
renovation detection module, a plurality of unpermitted renovation
candidates based on the detection indicia within the data set.
29. The method of claim 26 further comprising: a) calculating, by
an active renovation probability calculation module, a probability
that each unpermitted renovation event is an active renovation
event; b) accepting, by a prioritization validation module,
verified data regarding the unpermitted renovation event, active
renovation event, or time until the active renovation event is
complete; and c) feeding back the verified data from b) to the
renovation detection calculation module and active renovation
probability calculation module to improve their prediction over
time.
Description
CROSS-REFERENCE
[0001] This application claims the benefit of U.S. Provisional
Application No. 62/664,591, filed Apr. 30, 2018, U.S. Provisional
Application No. 62/671,957, filed May 15, 2018, U.S. Provisional
Application No. 62/695,564, filed Jul. 9, 2018, U.S. Provisional
Application No. 62/718,751, filed Aug. 14, 2018, and U.S.
Provisional Application No. 62/748,991, filed Oct. 22, 2018, each
of which are hereby incorporated by reference in their entirety
herein.
BACKGROUND OF THE INVENTION
[0002] The real estate improvement and repair expenditure market is
tremendous. In 2015 alone, improvement and maintenance expenditure
spent on both owner-occupied homes and rental units in the United
States reportedly came to $340 billion. The reported total sales of
home improvement retailers in 2017 the United States amounted to
about $373.6 billion. According to data compiled by Home
Improvement Research Institute, the home improvement market product
sales are expected to grow at an average 3.9% pace until at least
2020.
[0003] Renovations, which generally concern changes to, development
of, and/or replacement of an existing structure, are often
classified as either historical or active renovations. Historical
renovations generally refer to renovations that were finished at
some time in the past, either by the current owner or a previous
owner. Active renovations typically refer to renovations that are
in progress. These active renovations are typically performed by
people who buy a property, upgrade it, and resell it quickly
(sometimes referred to as "flippers") or by longer term home owners
who are upgrading their property either in preparation for selling
it or with the intent to keep it for a long time.
SUMMARY OF THE INVENTION
[0004] Most modifications to existing buildings require one or more
permits issued by the appropriate city or county authority. A
majority of such modifications or renovations, however, are
conducted without appropriate permits. When officials determine
that unpermitted work is performed, a penalty is added to the
permitting fee. The penalty can be directly proportional to the
scale, cost, or both of the renovation. In case of historical
renovations, the authority can further backdate the property tax
shortfall that should have been paid had the property been
reappraised.
[0005] Some unpermitted renovations do not meet appropriate codes
which have been imposed for safety of the owners, residents,
tenants, neighbors, and the general public. Further, unpermitted
renovations deny cities and authorities the renovation permit fees
and accurate property tax assessments necessary to fund such
enforcement and permitting processes. Indeed, in some cases the
city has no way to know a property has been upgraded (for property
assessment purposes) unless a permit is applied for, whether or not
it is subsequently granted. Hence, a need to identify past and
present unpermitted renovations, additions, modifications, and
properties which have been under-assessed for property tax purposes
is needed. Note that on occasion, renovations have been permitted
but for whatever reason not reassessed. In some situations, the
permit is not correct or there was some breakdown in the
reassessment process due to manual error or data inaccuracies.
Hence, a technological tool to address incorrectly permitted
renovations or incorrectly processed permits (which both lead to a
loss in property tax collection) is necessary.
[0006] In addition, it is a common occurrence that property owners
don't pay their full property tax for a variety of reasons. One of
the consequences of not identifying renovations is that homeowners
are not paying property tax on the full value of the improvement.
Identifying unpermitted renovations can be valuable for this
reason. It is also of value to point out a discrepancy in the
records, or a failure of process where a renovation was permitted
but the property was not reassessed.
[0007] In addition, even if a particular property is identified as
having been improperly renovated at some time in the past (meaning
that the current assessment is likely incorrect and undervalued),
there currently is no adequate tool to assist the appropriate
authorities to know more precisely when the renovation has taken
place. In many circumstances, the appropriate authority would like
to appropriately reassess the property to increase the amount of
property tax collected in the future. In addition, the authority
can charge "escape fees," which are essentially billing for back
taxes. Even if the improper assessment was the authority's mistake
(e.g., for incorrectly recording the square footage of the
property), authorities in some circumstances can charge up to four
years of escape fees. However, if the improper assessment was the
result of something done by the owners (e.g., when an owner fail to
apply for appropriate permits), the authority can charge escape
fees, in some cases, for up to eight prior years. Notably, escape
fees are generally not a penalty. In some circumstances, the
renovation that increased the property value was done only two
years before, only two years of escape fees can be charged. Hence,
it is advantageous to know when the renovation was done to help
assist the authority assess the appropriate amount of escape fees.
Furthermore, understanding when the renovation was done can further
assist the authority in circumstances when the property can have
changed owners one or more times, with (or when done fraudulently
without) re-assessment being done. In such situations, tools to
assist the proper authorities to determine who is liable for the
escape fees are needed.
[0008] Incomplete reporting, misreporting, or complete failure to
report real estate transactions (along with the consequential
avoidance of taxes and fees associated with ownership changes for
real estate properties such as Proposition 13 in California) can
also be an issue. Corporations that buy and flip a property can
comprise an S-corporation, a C-corporation, an LLC, a trust, or a
Title Holding Trust. In some situations, a Title Holding Trust
exists to make tracing the ownership of property more difficult
because the beneficiaries of trusts are not normally of public
record. Further, some property purchases or sales by the
corporation comprise a sale of the shareholding in the company
instead of a listed real property. In some circumstances under the
law, if the beneficial ownership of a trust or LLC changes by more
than 49.9% (that is, less than 50.1% remains in the hands of the
original owner(s)), the real property should be re-appraised for
the purposes of property tax. Such transactions, even if they occur
within multiple steps between multiple entities, can by law trigger
a re-appraisal. However, many people fail to report the change in
ownership in the required manner, thereby avoiding the
re-assessment. In related manners, a transaction can be reported,
but the value can be misreported or underreported. In sum, a tool
to enable authorities to re-assess the properties appropriately in
light of improperly reported real estate transactions to recover
lost revenue and increase current and future revenue is needed.
[0009] Moreover, incomplete reporting, misreporting, or complete
failure to report can also extend to determining residential status
or occupancy taxes for properties. For federal tax purposes, people
are allowed to have a primary residence and a vacation residence.
States have different laws and different income sources based on
residency such as vehicle registration fees and state income tax.
Mortgage interest deductions depend on properties being used
exclusively as residences. In some circumstances, there can be
scenarios where a person improperly (whether intentionally or
unintentionally) claim a property as a principal residence when it
is being rented out or when the person was actually a resident for
more than 50% of a tax year in a different state than the property
at issue. On a similar vein, a number of places charge lodging or
occupancy taxes for their properties. In some circumstances, the
owners of these places charging occupancy taxes are not properly
registered or remittance the appropriate amount of tax to the
appropriate authorities. Hence, a tool to enable authorities to
properly assess the properties for purposes of determining
residency and occupancy tax is needed.
[0010] Governmental entities have a primary interest in detecting
renovation events, but other parties might also find the
information useful. For example real estate agents and real estate
broker firms could be held liable for their part in selling a
property with unpermitted renovations. Title insurance companies
similarly would like to understand the renovation history of a
property. Property insurance companies would like to be informed
regularly of any increase in value of an insured property so as to
charge an appropriate premium. Lawyers, architects, builders and so
on might like to be made aware of potential clients who have to
undertake remedial repairs due to unpermitted renovations that are
not up to the appropriate code standards. Accordingly, a tool to
enable a plurality of parties to detect renovation events is
needed.
[0011] Public and private data sources can be useful in evaluating
whether an unpermitted renovation event--both past and present--has
occurred. For instance, data sources that reveal whether a property
of interest has been purchased by a corporation with a history of
frequent property turnover can indicate that the property currently
has one or more unpermitted active renovation events performed by a
flipper. The public database of permits can be checked to see if
one or more have been issued. The probability of an unpermitted
renovation in progress can then be increased by correlating other
information about the property or the owner such as social media
posts (e.g., Twitter, Facebook, Instagram, Facebook, Snapchat),
real estate-related listing services (e.g., multiple listing
services (MLS), Craigslist, AirBnB, Zillow, Redfin, Realtor), data
from building material suppliers or developers, credit card data,
customer information from supply companies (e.g., Home Depot,
Lowes, ACE, plumbing supplies, Lumber Liquidators Customer, Remodel
Works, Amazon, etc.), tax records, demolition orders, dumpster
orders, waste disposal records, portable restroom orders, or visual
inspection. These correlations can also be applied to traditional
owners. Public and private data sources can also be useful to
identify misreported or unreported changes in ownership during real
estate transactions. For instance, these data sources used to
identify beneficial ownership can include the required disclosure
of directors and shareholders of the corporation (which are often
involved in flipping properties). In instances where trusts are
involved, data sources in connection with the creation of the trust
and the related chain can be considered along with rental or MLS
data to identify any beneficial owners.
[0012] In another instance, any of the aforementioned sources can
be combined with other sources (e.g., previously issued permits,
lodged plans, registration of owners, lenders on properties, liens
on properties, historical real estate listings) to inform whether a
property of interest had one or more unpermitted historical
renovation events because of changes in the property's square
footage area (including current taxable square footage), valuation,
bedroom count, or bathroom count. Further, the US Postal Service
lists of addresses or any other address normalization service can
be used to correlate new addresses with renovations associated with
the building of unpermitted "Granny Flats", conversion of houses to
multiple apartments, or converting garages to apartments might be
detected.
[0013] Such techniques, when used in combination with trend machine
learning methods are highly capable of accurately determining the
probability of an unpermitted renovation event. Examples of such a
trend can include, for instance, that a property purchased by a
corporation is more likely to exhibit indicia of unpermitted
renovations, because such corporations often "flip" and turnover
such properties for profit. Such trends can be additionally
determined regarding the probability of an unpermitted renovation
with respect to other information about the property or the owner.
Correlation of these multiple sources of data with machine learned
trends can indicate that a property is being, or has been,
renovated, graded on a probability range from unlikely, through to
highly likely or virtually certain. This information can then be
used by the appropriate authority to target their enforcement.
[0014] Moreover, in some instances, the authorities who enforce the
various regulations have a limited number of inspectors and other
sources. Hence, in some instances, it can occur that the ability to
find such properties can exceed the ability of the appropriate
authorities to undertake inspection or enforcement action.
Accordingly, in some instances, it is beneficial to prioritize the
list of properties for the authorities to optimize or maximize the
efficiency of inspecting or taking enforcement action depending on
the kind of renovation. Data mining techniques can be used to
identify unpermitted renovations. In many instances, it is easier
for the authority to act upon an active renovation than a
historical one because the authority can just go to the property to
observe the activity as it is going on. It is particularly
advantageous to identify those properties while the renovation is
still in progress. Further, it can be advantageous to prioritize
renovation projects based on the estimated cost of the renovation,
since some authorities charge higher fees and penalties based on
the value of the renovation.
[0015] One aspect, disclosed herein is a non-transitory
computer-readable storage media encoded with a computer program
including instructions executable by a processor to create an
application to detect an unpermitted renovation event and validate
the detected event, the application comprising: a parameter setting
module that defines a data set to be evaluated; a plurality of data
ingestion interfaces, each interface connecting to a unique
external data source, each interface configured to perform at least
one of a natural language task process and a computer vision task
process to its data source to detect one or more unpermitted
renovation event indicia within the data set; a renovation
detection module that applies a machine learning algorithm to
identify an initial candidate based on the detection indicia within
the data set; a renovation probability calculation module that
calculates a probability that an unpermitted renovation event has
taken or is taking place at the initial candidate; and a validation
module that accepts verified data regarding the unpermitted
renovation event and feeds back the verified data to the renovation
probability calculation module to improve its prediction over
time.
[0016] Optionally, in some embodiments, each interface is
configured to perform a data mining task process to its data source
to detect one or more unpermitted renovation event indicia within
the data set. Optionally, in some embodiments, the data mining
process comprises a natural language task process, numerical data
mining task process, or a photographic data mining task process.
Optionally, in some embodiments, the data set is defined by at
least one of a street address, a parcel, a street, a lot, a zip
code, a county, a state, an area drawn on a map, an area within a
set radial distance from a location, coordinates set by one or more
satellites, an area within a set driving distance of a location, a
GPS point, and an area defined by at least three GPS points.
Optionally, in some embodiments, the natural language task process
comprises syntax interpretation, semantic interpretation, discourse
interpretation, or speech interpretation. Optionally, in some
embodiments, the syntax interpretation comprises lemmatization,
morphological segmentation, part-of-speech tagging, parsing,
sentence boundary disambiguation, stemming, word segmentation, or
terminology extraction. Optionally, in some embodiments, the
semantic interpretation comprises lexical semantics, machine
translation, named entity recognition, natural language generation,
natural language understanding, optical character recognition,
question answering, recognizing textual entailment, relationship
extraction, sentiment analysis, topic segmentation, or word sense
disambiguation. Optionally, in some embodiments, the discourse
interpretation comprises automatic summarization, coreference
resolution, or discourse analysis. Optionally, in some embodiments,
the speech interpretation comprises speech recognition, speech
segmentation, and text-to-speech. Optionally, in some embodiments,
the computer image task process comprises object recognition,
object identification, object detection, content-based image
retrieval, optical character recognition, facial recognition, shape
recognition, egomotion, object tracking, optical flow, or any
combination thereof. Optionally, in some embodiments, the external
data source comprises city property records, county property
records, city permit records, county permit records, post office
address database, state business records, historical real estate
listings, rental listings, demolition orders, dumpster orders,
portable restroom orders, customer account information from third
party companies, social media, phone records, address records,
historical credit card history purchase records, satellite images,
tax records, street views, online photographs, online videos, signs
outside a property, image processing of satellite, street view
images, or the Internet. Optionally, in some embodiments, the
detection of one or more unpermitted renovation event indicia
comprises determining a square footage of a property, a change in
the square footage of a property, a bed count of a property, a
change in a bed count of a property, a bathroom count of a
property, a change in a bathroom count of a property, a valuation
of a property, a change in a valuation of the property, ownership
of a property, a corporation owning a property, an owner with a
history of flipping one or more properties, lenders on a property,
or liens on a property. Optionally, in some embodiments, the
calculation comprises applying an increased weighted factor that
the unpermitted renovation event has taken place if a property is
owned by a corporation. Optionally, in some embodiments, the
calculation comprises applying an increased weighted factor that
the unpermitted renovation event has taken place if one or more
corporate officers have previously flipped properties. Optionally,
in some embodiments, the calculation comprises applying an
increased weighted factor that the unpermitted renovation event has
taken place if a property owner's social media displays
renovations. Optionally, in some embodiments, the calculation
comprises applying an increased weighted factor that the
unpermitted renovation event has taken place if a real estate
listing displays renovations. Optionally, in some embodiments, the
calculation comprises calculating whether a probability threshold
has been met. Optionally, in some embodiments, the unpermitted
renovation event comprises violations of building codes, past
unpermitted renovations, present unpermitted renovations, additions
to a property, upgrades to a property, or modifications to a
property. Optionally, in some embodiments, the verified data is
acquired by a public official inspecting a candidate property.
Optionally, in some embodiments, the verified data is an issued
permit for the renovated event at the initial candidate.
Optionally, in some embodiments, the media further comprises a
secondary screening module, wherein if the probability calculation
module calculates a probability in excess of a predetermined
threshold, the secondary screening module proceeds to conduct
further screening procedures.
[0017] Another aspect, disclosed herein is a non-transitory
computer-readable storage media encoded with a computer program
including instructions executable by a processor to perform a
method to detect an unpermitted renovation event and validate the
detected event, the method comprising: a parameter setting module
defining a data set to be evaluated; a plurality of data ingestion
interfaces, each interface connecting to a unique external data
source, each interface performing at least one of a natural
language task process and a computer vision task process to its
data source to detect one or more unpermitted renovation event
indicia within the data set; a renovation detection module that
applies a machine learning algorithm to identify an initial
candidate based on the detection indicia within the data set; a
renovation probability calculation module calculating a probability
that an unpermitted renovation event has taken or is taking place
at the initial candidate; a validation module that accepts verified
data regarding the unpermitted renovation event and feeds back the
verified data to the renovation probability calculation module to
improve its prediction over time. Optionally, in some embodiments,
each interface is configured to perform a data mining task process
to its data source to detect one or more unpermitted renovation
event indicia within the data set. Optionally, in some embodiments,
the data mining process comprises a natural language task process,
numerical data mining task process, or a photographic data mining
task process.
[0018] Another aspect, disclosed herein is a system comprising a
non-transitory computer-readable storage media encoded with a
computer program including instructions executable by a processor
to create an application to detect an unpermitted renovation event
and validate the detected event, the application comprising: a
parameter setting module that defines a data set to be evaluated; a
plurality of data ingestion interfaces, each interface connecting
to a unique external data source, each interface configured to
perform at least one of a natural language task process and a
computer vision task process to its data source to detect one or
more unpermitted renovation event indicia within the data set; a
renovation detection module that applies a machine learning
algorithm to identify an initial candidate based on the detection
indicia within the data set; a renovation probability calculation
module that calculates a probability that an unpermitted renovation
event has taken or is taking place at the initial candidate; a
validation module that accepts verified data regarding the
unpermitted renovation event and feeds back the verified data to
the renovation probability calculation module to improve its
prediction over time. Optionally, in some embodiments, each
interface is configured to perform a data mining task process to
its data source to detect one or more unpermitted renovation event
indicia within the data set. Optionally, in some embodiments, the
data mining process comprises a natural language task process,
numerical data mining task process, or a photographic data mining
task process.
[0019] Another aspect, disclosed herein is a platform comprising a
non-transitory computer-readable storage media encoded with a
computer program including instructions executable by a processor
to create an application to detect an unpermitted renovation event
and validate the detected event, the application comprising: a
parameter setting module that defines a data set to be evaluated; a
plurality of data ingestion interfaces, each interface connecting
to a unique external data source, each interface configured to
perform at least one of a natural language task process and a
computer vision task process to its data source to detect one or
more unpermitted renovation event indicia within the data set; a
renovation detection module that applies a machine learning
algorithm to identify an initial candidate based on the detection
indicia within the data set; a renovation probability calculation
module that calculates a probability that an unpermitted renovation
event has taken or is taking place at the initial candidate; a
validation module that accepts verified data regarding the
unpermitted renovation event and feeds back the verified data to
the renovation probability calculation module to improve its
prediction over time. Optionally, in some embodiments, each
interface is configured to perform a data mining task process to
its data source to detect one or more unpermitted renovation event
indicia within the data set. Optionally, in some embodiments, the
data mining process comprises a natural language task process,
numerical data mining task process, or a photographic data mining
task process. Optionally, the data mining process comprises a
predetermined algorithm. Optionally, the data mining process
comprises a machine learning based algorithm. Optionally, in some
embodiments, the data mining process comprises other forms of
interpretation of data.
[0020] Another aspect, disclosed herein is a non-transitory
computer-readable storage media encoded with a computer program
including instructions executable by a processor to create an
application to prioritize inspection of unpermitted renovation
candidates and validate the prioritization, the application
comprising: a parameter setting module that defines a data set to
be evaluated; a plurality of data ingestion interfaces, each
interface connected to a unique external data source, each
interface configured to perform a data mining task process to its
data source to detect one or more unpermitted renovation event
indicia within the data set; a renovation detection module that
applies a machine learning algorithm to identify a plurality of
unpermitted renovation candidates based on the detection indicia
within the data set; a renovation probability calculation module
that calculates a probability that an unpermitted renovation event
has taken or is taking place at each unpermitted renovation
candidate; an active renovation probability calculation module that
calculates a probability that each unpermitted renovation event is
an active renovation event; an active renovation completion
estimator module that assigns a value estimating the time until the
active renovation event is completed; an unpermitted renovation
candidate to inspector location distance calculation module that
calculates a distance between the plurality of unpermitted
renovation candidates and a location of an inspector; an inspection
prioritization module that provides an order for the inspector to
prioritize inspecting the plurality of unpermitted renovation
candidates based on the probability that the unpermitted renovation
event is an active renovation event, the estimated value regarding
the time until the active renovation event is completed, and the
distance between the plurality of unpermitted renovation candidates
and the location of the inspector; and a prioritization validation
module that accepts verified data regarding the unpermitted
renovation event, active renovation event, or time until the active
renovation event is complete and feeds back the verified data to
the renovation detection calculation module and active renovation
probability calculation module to improve their prediction over
time. Optionally, in some embodiments, the storage media further
comprises a renovation value calculation module that calculates a
value of the unpermitted renovation event. Optionally, in some
embodiments, the inspection prioritization module provides an order
for the inspector to prioritize inspecting the plurality of
unpermitted renovations candidates based on the calculated value of
the unpermitted renovation event. Optionally, in some embodiments,
the calculated value of the unpermitted renovation event comprises
considering the unpermitted renovation event's impact on property
tax values, dollars spent on the unpermitted renovation event, or
amount of penalties or fees. Optionally, in some embodiments, the
prioritization validation module accepts verified data regarding
the value of the unpermitted renovation event and feeds back the
verified data to the renovation value calculation module to improve
its prediction over time. Optionally, in some embodiments, the
prioritization comprises applying an increased priority factor as
the value of the unpermitted event increases.
[0021] Optionally, in some embodiments, the active renovation
probability calculation comprises applying an increased weighted
factor that an active renovation event has taken place if a
property is owned by a corporation. Optionally, in some
embodiments, the active renovation probability calculation
comprises applying an increased weighted factor that an active
renovation event has taken place if one or more corporate officers
have previously flipped properties. Optionally, in some
embodiments, the active renovation probability calculation
comprises applying an increased weighted factor that an active
renovation event has taken place if a property owner's social media
displays renovations. Optionally, in some embodiments, the active
renovation probability calculation comprises applying an increased
weighted factor that an active renovation event has taken place if
a real estate listing displays renovations. Optionally, in some
embodiments, the active renovation probability calculation
comprises applying an increased weighted factor that an active
renovation event has taken place if an unpermitted renovation
candidate was acquired more recently. Optionally, in some
embodiments, the prioritization comprises applying an increased
priority factor as the probability than an unpermitted renovation
event is an active renovation event. The prioritization comprises
applying an increased priority factor as the value estimating the
time until the active renovation event is completed approaches
zero. Optionally, in some embodiments, the prioritization comprises
applying an increased priority factor as the value estimating the
time until the active renovation event is completed approaches
zero. Optionally, in some embodiments, the storage media further
comprises a renovation value calculation module that calculates a
value of the unpermitted renovation event. Optionally, in some
embodiments, the inspection prioritization module provides an order
for the inspector to prioritize inspecting the plurality of
unpermitted renovations candidates based on the calculated value of
the unpermitted renovation event. Optionally, in some embodiments,
the calculated value of the unpermitted renovation event comprises
considering the unpermitted renovation event's impact on property
tax values, dollars spent on the unpermitted renovation event, or
amount of penalties or fees. Optionally, in some embodiments, the
prioritization validation module accepts verified data regarding
the value of the unpermitted renovation event and feeds back the
verified data to the renovation value calculation module to improve
its prediction over time. Optionally, in some embodiments, the
prioritization comprises applying an increased priority factor as
the value of the unpermitted event increases.
[0022] Another aspect, disclosed herein is a non-transitory
computer-readable storage media encoded with a computer program
including instructions executable by a processor to perform a
method to prioritize inspection of unpermitted renovation
candidates and validate the prioritization, the method comprising:
a parameter setting module that defines a data set to be evaluated;
a plurality of data ingestion interfaces, each interface connected
to a unique external data source, each interface configured to
perform a data mining task process to its data source to detect one
or more unpermitted renovation event indicia within the data set; a
renovation detection module that applies a machine learning
algorithm to identify a plurality of unpermitted renovation
candidates based on the detection indicia within the data set; a
renovation probability calculation module that calculates a
probability that an unpermitted renovation event has taken or is
taking place at each unpermitted renovation candidate; an active
renovation probability calculation module that calculates a
probability that each unpermitted renovation event is an active
renovation event; an active renovation completion estimator module
that assigns a value estimating the time until the active
renovation event is completed; an unpermitted renovation candidate
to inspector location distance calculation module that calculates a
distance between the plurality of unpermitted renovation candidates
and a location of an inspector; an inspection prioritization module
that provides an order for the inspector to prioritize inspecting
the plurality of unpermitted renovation candidates based on the
probability that the unpermitted renovation event is an active
renovation event, the estimated value regarding the time until the
active renovation event is completed, and the distance between the
plurality of unpermitted renovation candidates and the location of
the inspector; and a prioritization validation module that accepts
verified data regarding the unpermitted renovation event, active
renovation event, or time until the active renovation event is
complete and feeds back the verified data to the renovation
detection calculation module and active renovation probability
calculation module to improve their prediction over time.
Optionally, in some embodiments, the method further comprises a
renovation value calculation module that calculates a value of the
unpermitted renovation event. Optionally, in some embodiments, the
inspection prioritization module provides an order for the
inspector to prioritize inspecting the plurality of unpermitted
renovations candidates based on the calculated value of the
unpermitted renovation event. Optionally, in some embodiments, the
calculated value of the unpermitted renovation event comprises
considering the unpermitted renovation event's impact on property
tax values, dollars spent on the unpermitted renovation event, or
amount of penalties or fees. Optionally, in some embodiments, the
prioritization validation module accepts verified data regarding
the value of the unpermitted renovation event and feeds back the
verified data to the renovation value calculation module to improve
its prediction over time. Optionally, in some embodiments, the
prioritization comprises applying an increased priority factor as
the value of the unpermitted event increases.
[0023] Another aspect, disclosed herein is a system comprising a
non-transitory computer-readable storage media encoded with a
computer program including instructions executable by a processor
to create an application to prioritize inspection of unpermitted
renovation candidates and validate the prioritization, the
application comprising: a parameter setting module that defines a
data set to be evaluated; a plurality of data ingestion interfaces,
each interface connected to a unique external data source, each
interface configured to perform a data mining task process to its
data source to detect one or more unpermitted renovation event
indicia within the data set; a renovation detection module that
applies a machine learning algorithm to identify a plurality of
unpermitted renovation candidates based on the detection indicia
within the data set; a renovation probability calculation module
that calculates a probability that an unpermitted renovation event
has taken or is taking place at each unpermitted renovation
candidate; an active renovation probability calculation module that
calculates a probability that each unpermitted renovation event is
an active renovation event; an active renovation completion
estimator module that assigns a value estimating the time until the
active renovation event is completed; an unpermitted renovation
candidate to inspector location distance calculation module that
calculates a distance between the plurality of unpermitted
renovation candidates and a location of an inspector; an inspection
prioritization module that provides an order for the inspector to
prioritize inspecting the plurality of unpermitted renovation
candidates based on the probability that the unpermitted renovation
event is an active renovation event, the estimated value regarding
the time until the active renovation event is completed, and the
distance between the plurality of unpermitted renovation candidates
and the location of the inspector; and a prioritization validation
module that accepts verified data regarding the unpermitted
renovation event, active renovation event or time until the active
renovation event is complete and feeds back the verified data to
the renovation detection calculation module and active renovation
probability calculation module to improve their prediction over
time. Optionally, in some embodiments, the storage media further
comprises a renovation value calculation module that calculates a
value of the unpermitted renovation event. Optionally, in some
embodiments, the inspection prioritization module provides an order
for the inspector to prioritize inspecting the plurality of
unpermitted renovations candidates based on the calculated value of
the unpermitted renovation event. Optionally, in some embodiments,
the calculated value of the unpermitted renovation event comprises
considering the unpermitted renovation event's impact on property
tax values, dollars spent on the unpermitted renovation event, or
amount of penalties or fees. Optionally, in some embodiments, the
prioritization validation module accepts verified data regarding
the value of the unpermitted renovation event and feeds back the
verified data to the renovation value calculation module to improve
its prediction over time. Optionally, in some embodiments, the
prioritization comprises applying an increased priority factor as
the value of the unpermitted event increases.
[0024] Another aspect, disclosed herein is a platform comprising a
non-transitory computer-readable storage media encoded with a
computer program including instructions executable by a processor
to create an application to prioritize inspection of unpermitted
renovation candidates and validate the prioritization, the
application comprising: a parameter setting module that defines a
data set to be evaluated; a plurality of data ingestion interfaces,
each interface connected to a unique external data source, each
interface configured to perform a data mining task process to its
data source to detect one or more unpermitted renovation event
indicia within the data set; a renovation detection module that
applies a machine learning algorithm to identify a plurality of
unpermitted renovation candidates based on the detection indicia
within the data set; a renovation probability calculation module
that calculates a probability that an unpermitted renovation event
has taken or is taking place at each unpermitted renovation
candidate; an active renovation probability calculation module that
calculates a probability that each unpermitted renovation event is
an active renovation event; an active renovation completion
estimator module that assigns a value estimating the time until the
active renovation event is completed; an unpermitted renovation
candidate to inspector location distance calculation module that
calculates a distance between the plurality of unpermitted
renovation candidates and a location of an inspector; an inspection
prioritization module that provides an order for the inspector to
prioritize inspecting the plurality of unpermitted renovation
candidates based on the probability that the unpermitted renovation
event is an active renovation event, the estimated value regarding
the time until the active renovation event is completed, and the
distance between the plurality of unpermitted renovation candidates
and the location of the inspector; and a prioritization validation
module that accepts verified data regarding the unpermitted
renovation event, active renovation event, or time until the active
renovation event is complete and feeds back the verified data to
the renovation detection calculation module and active renovation
probability calculation module to improve their prediction over
time. Optionally, in some embodiments, the storage media further
comprises a renovation value calculation module that calculates a
value of the unpermitted renovation event. Optionally, in some
embodiments, the inspection prioritization module provides an order
for the inspector to prioritize inspecting the plurality of
unpermitted renovations candidates based on the calculated value of
the unpermitted renovation event. Optionally, in some embodiments,
the calculated value of the unpermitted renovation event comprises
considering the unpermitted renovation event's impact on property
tax values, dollars spent on the unpermitted renovation event, or
amount of penalties or fees. Optionally, in some embodiments, the
prioritization validation module accepts verified data regarding
the value of the unpermitted renovation event and feeds back the
verified data to the renovation value calculation module to improve
its prediction over time. Optionally, in some embodiments, the
prioritization comprises applying an increased priority factor as
the value of the unpermitted event increases.
[0025] Another aspect provided herein is a non-transitory
computer-readable storage media encoded with a computer program
including instructions executable by a processor to create an
application to detect an improper real estate transfer event, the
application comprising: a parameter setting module that defines a
data set to be evaluated; a plurality of data ingestion interfaces,
each interface connecting to a unique external data source, each
interface configured to perform a data mining task process to its
data source to detect one or more real estate transfer indicia
within the data set; an improper transfer detection module that
applies a machine learning algorithm to identify an initial
candidate based on the real estate transfer indicia within the data
set; an improper real estate transfer probability calculation
module that calculates a probability that the improper real estate
transfer event has taken place at the initial candidate; a
validation module that accepts verified data regarding the real
estate transfer event and feeds back the verified data to the
improper real estate transfer probability calculation module to
improve its calculation over time.
[0026] Optionally, in some embodiments, the real estate indicia
comprises a valuation of a property, a change in a valuation of the
property, a current ownership of the property, a past ownership of
the property, a lender on a property, an ownership percentage of
the property, or one or more liens on a property. Optionally, in
some embodiments, the machine learning algorithm identifies an
initial candidate if at least one of the current ownership and the
past ownership of the initial candidate comprises a corporation.
Optionally, in some embodiments, the machine learning algorithm
identifies an initial candidate if the corporation comprises a
title holding trust. Optionally, in some embodiments, the machine
learning algorithm identifies an initial candidate if the ownership
percentage of the property changes by more than 49.9%. Optionally,
in some embodiments, the calculation comprises applying an
increased weighted factor that the improper real estate transfer
event has taken place if at least one of the current ownership and
the past ownership of the initial candidate comprises a
corporation. Optionally, in some embodiments, the calculation
comprises applying an increased weighted factor that the improper
real estate transfer event has taken place if the corporation
comprises a title holding trust. Optionally, in some embodiments,
the calculation comprises applying an increased weighted factor
that the improper real estate transfer event has taken place if the
ownership percentage of the property changes by more than 49.9%.
Optionally, in some embodiments, the calculation comprises
calculating whether a probability threshold has been met.
Optionally, in some embodiments, the verified data is acquired by a
public official inspecting the candidate property. Optionally, in
some embodiments, the data set is defined by at least one of a
street address, a parcel, a street, a lot, a zip code, a county, a
state, an area drawn on a map, an area within a set radial distance
from a location, coordinates set by one or more satellites, an area
within a set driving distance of a location, a GPS point, and an
area defined by at least three GPS points. Optionally, in some
embodiments, the data mining task process comprises a natural
language process, numerical data mining process, or a photographic
data mining task process. Optionally, in some embodiments, the
natural language task process comprises syntax interpretation,
semantic interpretation, discourse interpretation, or speech
interpretation. Optionally, in some embodiments, the syntax
interpretation comprises lemmatization, morphological segmentation,
part-of-speech tagging, parsing, sentence boundary disambiguation,
stemming, word segmentation, or terminology extraction. Optionally,
in some embodiments, the semantic interpretation comprises lexical
semantics, machine translation, named entity recognition, natural
language generation, natural language understanding, optical
character recognition, question answering, recognizing textual
entailment, relationship extraction, sentiment analysis, topic
segmentation, or word sense disambiguation. Optionally, in some
embodiments, the discourse interpretation comprises automatic
summarization, coreference resolution, or discourse analysis.
Optionally, in some embodiments, the speech interpretation
comprises speech recognition, speech segmentation, and
text-to-speech. Optionally, in some embodiments, the external data
source comprises city property records, county property records,
city permit records, county permit records, post office address
database, state business records, historical real estate listings,
rental listings, demolition orders, dumpster orders, portable
restroom orders, customer account information from third party
companies, social media, phone records, address records, historical
credit card history purchase records, satellite images, tax
records, street views, online photographs, online videos, signs
outside a property, demolition orders, dumpster orders, portable
restroom orders, or the Internet. Optionally, in some embodiments,
the application further comprises a historical transfer database
receiving and storing a plurality of the real estate transfer
indicia from the plurality of data ingestion interfaces, and
wherein the historical transfer database transmits one or more of
the plurality of stored real estate transfer indicia to the
improper transfer detection module. Optionally, in some
embodiments, the plurality of stored real estate transfer indicia
comprises a sequence of transfers regarding a real estate unit.
Optionally, in some embodiments, the historical transfer database
further receives a plurality of the initial candidates from the
improper real estate transfer detection module and appends the each
of the initial candidates to at least one of the stored real estate
transfer indicia. Optionally, in some embodiments, the improper
transfer detection module applies the machine learning algorithm to
identify the initial candidate based further on the initial
candidates appended to the plurality of stored real estate transfer
indicia.
[0027] Another aspect provided herein is a non-transitory
computer-readable storage media encoded with a computer program
including instructions executable by a processor to create an
application to determine when one or more unpermitted renovation
events has taken place to an unpermitted renovation candidate, the
application comprising: an unpermitted renovation candidate module
that presents an unpermitted renovation candidate; a parameter
setting module that defines a data set to be evaluated; a set of
first data ingestion interfaces, each first interface connecting to
a first data source, each interface configured to perform a data
mining task process to a first data source to determine an initial
time range within the data set when at least one unpermitted
renovation event has taken place at the unpermitted renovation
candidate; a set of second data ingestion interfaces, each second
interface connecting to a second data source, each interface
configured to perform a data mining task process to the second data
source to detect one or more unpermitted renovation timing indicia
within the data set when the at least one unpermitted renovation
event has taken place at the unpermitted renovation candidate; a
renovation timing estimation module that applies a machine learning
algorithm to present a refined renovation time range based on the
detected initial time range and the detected unpermitted renovation
timing indicia; and a validation module that accepts verified data
regarding the timing of the unpermitted renovation event and feeds
back the verified data to the renovation timing estimation module
to improve its prediction over time.
[0028] In some embodiments, the initial time range comprises a time
range from a current time to when the unpermitted renovation event
was assessed according to the first data source. In some
embodiments, the first data source comprises city property records,
county property records, city permit records, county permit
records, and state business records. In some embodiments, the
second data source comprises public sources, licensed data feeds,
sources depicting historical water usage at the unpermitted
renovation candidate, sources depicting historical energy usage at
the unpermitted renovation candidate, contractor web sites, Yelp,
Craigslist, Wayback Machine, financial documents, photographs from
aerial surveys, Google Earth, Google Streetview, rental records for
dumpsters, rental records for portable restrooms, serial numbers,
manufacturer warranty records, Home Owner's Association records,
historical real estate listings, rental listings, demolition
orders, dumpster orders, portable restroom orders, customer account
information from third party companies, social media, phone
records, address records, historical credit card history purchase
records, satellite images, tax records, street views, online
photographs, online videos, signs outside a property, demolition
orders, dumpster orders, portable restroom orders, or the Internet.
In some embodiments, the unpermitted renovation timing indicia
comprises increase in water usage, decrease in water usage,
increase in energy usage, decrease in energy usage, permanent
change in water usage, permanent change in energy usage, records of
renovations from Internet sources, documentation reflecting
refinanced mortgages, documentation reflecting home equity lines of
credit, photographs depicting structural changes, records
reflecting renovation work, records reflecting renovation waste,
serial numbers reflecting new appliances, windows, or air
conditioners, or manufacturer warranty records reflecting dates of
installation. In some embodiments, the refined renovation time
range comprises a narrower time range than the initial time range.
In some embodiments, the application further comprises a second
data source filter module configured to allow a user to filter the
second data mining task process to the second data source.
[0029] Another aspect provided herein is a non-transitory
computer-readable storage media encoded with a computer program
including instructions executable by a processor to create an
application to detect an improper residency status for a real
estate property, the application comprising: a parameter setting
module that defines a data set to be evaluated; a plurality of data
ingestion interfaces, each interface connecting to a unique
external data source, each interface configured to perform a data
mining task process to its data source to detect one or more
improper residency indicia within the data set; an improper
residency detection module that applies a machine learning
algorithm to identify an initial candidate based on the improper
residency indicia within the data set; a residency probability
calculation module that calculates a probability that the initial
candidate has an improper residency status; and a validation module
that accepts verified data regarding the residency status and feeds
back the verified data to the improper residency probability
calculation module to improve its calculation over time.
[0030] In some embodiments, the data set is defined by at least one
of a street address, a parcel, a street, a lot, a zip code, a
county, a state, an area drawn on a map, an area within a set
radial distance from a location, coordinates set by one or more
satellites, an area within a set driving distance of a location, a
GPS point, and an area defined by at least three GPS points. In
some embodiments, the data mining task process comprises a natural
language process, numerical data mining process, a photographic
data mining task process, or any combination thereof. In some
embodiments, the natural language task process comprises syntax
interpretation, semantic interpretation, discourse interpretation,
speech interpretation, or any combination thereof. In some
embodiments, the syntax interpretation comprises lemmatization,
morphological segmentation, part-of-speech tagging, parsing,
sentence boundary disambiguation, stemming, word segmentation,
terminology extraction, or any combination thereof. In some
embodiments, the semantic interpretation comprises lexical
semantics, machine translation, named entity recognition, natural
language generation, natural language understanding, optical
character recognition, question answering, recognizing textual
entailment, relationship extraction, sentiment analysis, topic
segmentation, word sense disambiguation, or any combination
thereof. In some embodiments, the discourse interpretation
comprises automatic summarization, coreference resolution,
discourse analysis, or any combination thereof. In some
embodiments, the speech interpretation comprises speech
recognition, speech segmentation, text-to-speech, or both. In some
embodiments, the external data source comprises city property
records, county property records, city permit records, county
permit records, post office address database, state business
records, historical real estate listings, rental listings,
demolition orders, dumpster orders, portable restroom orders,
customer account information from third party companies, social
media, phone records, address records, historical credit card
history purchase records, satellite images, tax records, street
views, online photographs, online videos, signs outside a property,
demolition orders, dumpster orders, portable restroom orders, the
Internet, or any combination thereof. In some embodiments, the
detection of one or more improper residency indicia comprises water
usage change, electricity usage change, gas usage change, street
parking occupancy change, driveway parking occupancy change,
package delivery frequency change, window adjustment frequency
change, visible room light frequency change, a street-side trash
can placement frequency change, a mailbox flag status frequency
change, a garage door status frequency change, a frequency of phone
calls, a frequency of credit card purchases, or any combination
thereof.
[0031] Another aspect provided herein is a non-transitory
computer-readable storage media encoded with a computer program
including instructions executable by a processor to create an
application to detect an improper occupancy tax status for a real
estate property, the application comprising: a parameter setting
module that defines a data set to be evaluated; a plurality of data
ingestion interfaces, each interface connecting to a unique
external data source, each interface configured to perform a data
mining task process to its data source to detect one or more
improper occupancy tax indicia within the data set; an improper
occupancy tax detection module that applies a machine learning
algorithm to identify an initial candidate based on the improper
occupancy tax indicia within the data set; an occupancy tax
probability calculation module that calculates a probability that
the initial candidate has an improper occupancy tax status; and a
validation module that accepts verified data regarding the
occupancy tax status and feeds back the verified data to the
improper occupancy tax probability calculation module to improve
its calculation over time.
[0032] In some embodiments, the data set is defined by at least one
of a street address, a parcel, a street, a lot, a zip code, a
county, a state, an area drawn on a map, an area within a set
radial distance from a location, coordinates set by one or more
satellites, an area within a set driving distance of a location, a
GPS point, and an area defined by at least three GPS points. In
some embodiments, the data mining task process comprises a natural
language process, numerical data mining process, a photographic
data mining task process, or any combination thereof. In some
embodiments, the natural language task process comprises syntax
interpretation, semantic interpretation, discourse interpretation,
speech interpretation, or any combination thereof. In some
embodiments, the syntax interpretation comprises lemmatization,
morphological segmentation, part-of-speech tagging, parsing,
sentence boundary disambiguation, stemming, word segmentation,
terminology extraction, or any combination thereof. In some
embodiments, the semantic interpretation comprises lexical
semantics, machine translation, named entity recognition, natural
language generation, natural language understanding, optical
character recognition, question answering, recognizing textual
entailment, relationship extraction, sentiment analysis, topic
segmentation, word sense disambiguation, or any combination
thereof. In some embodiments, the discourse interpretation
comprises automatic summarization, coreference resolution,
discourse analysis, or any combination thereof. In some
embodiments, the speech interpretation comprises speech
recognition, speech segmentation, and text-to-speech, or any
combination thereof. In some embodiments, the external data source
comprises AirBnB, VRBO, city property records, county property
records, city permit records, county permit records, post office
address database, state business records, historical real estate
listings, rental listings, demolition orders, dumpster orders,
portable restroom orders, customer account information from third
party companies, social media, phone records, address records,
historical credit card history purchase records, satellite images,
tax records, street views, online photographs, online videos, signs
outside a property, demolition orders, dumpster orders, portable
restroom orders, the Internet, or any combination thereof. In some
embodiments, the detection of one or more improper occupancy tax
indicia comprises water usage change, electricity usage change, gas
usage change, street parking occupancy change, driveway parking
occupancy change, package delivery frequency change, window
adjustment frequency change, visible room light frequency change, a
street-side trash can placement frequency change, a mailbox flag
status frequency change, a garage door status frequency change, a
frequency of phone calls, a frequency of credit card purchases, or
any combination thereof.
[0033] Another aspect provided herein is a non-transitory
computer-readable storage media encoded with a computer program
including instructions executable by a processor to create an
application to detect an unpermitted renovation event and validate
the detected event, the application comprising: a parameter setting
module that defines a data set to be evaluated; a plurality of data
ingestion interfaces, each interface connecting to a unique
external data source, each interface configured to perform a data
mining task process to its data source to detect one or more
unpermitted renovation event indicia within the data set; a
renovation detection module that applies a machine learning
algorithm to identify an initial candidate based on the detection
indicia within the data set; a renovation probability calculation
module that calculates a probability that an unpermitted renovation
event has taken or is taking place at the initial candidate; and a
validation module that accepts verified data regarding the
unpermitted renovation event and feeds back the verified data to
the renovation probability calculation module to improve its
prediction over time.
[0034] In some embodiments, the data set is defined by at least one
of a street address, a parcel, a street, a lot, a zip code, a
county, a state, an area drawn on a map, an area within a set
radial distance from a location, coordinates set by one or more
satellites, an area within a set driving distance of a location, a
GPS point, or an area defined by at least three GPS points. In some
embodiments the data mining task process comprises a natural
language process, numerical data mining process, or a photographic
data mining task process. In some embodiments the natural language
task process comprises syntax interpretation, semantic
interpretation, discourse interpretation, or speech interpretation.
In some embodiments the syntax interpretation comprises
lemmatization, morphological segmentation, part-of-speech tagging,
parsing, sentence boundary disambiguation, stemming, word
segmentation, or terminology extraction. In some embodiments the
semantic interpretation comprises lexical semantics, machine
translation, named entity recognition, natural language generation,
natural language understanding, optical character recognition,
question answering, recognizing textual entailment, relationship
extraction, sentiment analysis, topic segmentation, RNN language
modeling, Deep Learning, or word sense disambiguation. In some
embodiments the discourse interpretation comprises automatic
summarization, coreference resolution, or discourse analysis. In
some embodiments the speech interpretation comprises speech
recognition, speech segmentation, and text-to-speech. In some
embodiments the external data source comprises city property
records, county property records, city permit records, county
permit records, post office address database, state business
records, historical real estate listings, rental listings,
demolition orders, dumpster orders, portable restroom orders,
customer account information from third party companies, social
media, phone records, address records, historical credit card
history purchase records, satellite images, tax records, street
views, online photographs, online videos, signs outside a property,
aircraft photos, parking infringements, noise complaints, landfill
records, traffic permits, or the Internet. In some embodiments the
detection of one or more unpermitted renovation event indicia
comprises determining a square footage of a property, a change in
the square footage of a property, a bed count of a property, a
change in a bed count of a property, a bathroom count of a
property, a change in a bathroom count of a property, a change in a
parking count of a property, a change in a garage size of a
property, a valuation of a property, a change in a valuation of the
property, ownership of a property, a corporation owning a property,
an owner with a history of flipping one or more properties, an
owner with a history of unpermitted renovations one or more
properties, lenders on a property, a change in the rent value of
the property, a discrepancy between a current and a prior rental
listing, or a lien on a property. In some embodiments the
calculation comprises applying an increased weighted factor that
the unpermitted renovation event has taken place, providing an
indicator for a machine learning algorithm, or both if (a) a
property is owned by a corporation, (b) one or more corporate
officers has previously flipped properties, (c) a property owner's
social media displays renovations, (d) a real estate listing
displays renovations, or (e) any combination thereof. In some
embodiments the calculation comprises calculating whether a
probability threshold has been met. In some embodiments the
verified data is acquired by a public official inspecting a
candidate property. In some embodiments the verified data is an
issued permit for the renovated event at the initial candidate. In
some embodiments, the media further comprises a secondary screening
module, wherein if the probability calculation module calculates a
probability in excess of a predetermined threshold, the secondary
screening module proceeds to conduct further screening procedures.
In some embodiments the validation module further provides at least
one of the probability and the initial candidate to an inspector.
In some embodiments the renovation detection module identifies a
plurality of unpermitted renovation candidates based on the
detection indicia within the data set. In some embodiments the
application further comprises: an active renovation probability
calculation module that calculates a probability that each
unpermitted renovation event is an active renovation event; and a
prioritization validation module that accepts verified data
regarding the unpermitted renovation event, active renovation
event, or time until the active renovation event is complete and
feeds back the verified data to the renovation detection
calculation module and active renovation probability calculation
module to improve their prediction over time. In some embodiments,
the media further comprises an active renovation completion
estimator module that assigns a value estimating the time until the
active renovation event is completed. In some embodiments, the
media further comprises an unpermitted renovation candidate to
inspector location distance calculation module that calculates a
distance between the plurality of unpermitted renovation candidates
and a location of an inspector. In some embodiments, the media
further comprises an inspection prioritization module that provides
an order for the inspector to prioritize inspecting the plurality
of unpermitted renovation candidates based on the probability that
the unpermitted renovation event is an active renovation event, the
estimated value regarding the time until the active renovation
event is completed, and the distance between the plurality of
unpermitted renovation candidates and the location of the
inspector. In some embodiments the active renovation probability
calculation comprises applying an increased weighted factor that an
active renovation event has taken place if (a) a property is owned
by a corporation, (b) one or more corporate officers has previously
flipped properties, (c) a property owner's social media displays
renovations, (d) a real estate listing displays renovations, (e) if
an unpermitted renovation candidate was acquired more recently, or
(f) any combination thereof. In some embodiments the prioritization
comprises applying an increased priority factor as (a) the value
estimating the time until the active renovation event is completed
approaches zero, (b) the calculated distance between the plurality
of unpermitted renovation candidates and a location of an inspector
approaches zero, or (c) any combination thereof. In some
embodiments the prioritization validation module accepts verified
data regarding the value of the unpermitted renovation event and
feeds back the verified data regarding the value of the unpermitted
renovation event to the renovation value calculation module to
improve its prediction over time.
[0035] Another aspect provided herein is a computer implemented
system comprising: a computer-readable storage device coupled to at
least one processor and having instructions stored thereon which,
when executed by the at least one processor, causes the at least
one processor to perform operations comprising: defining, by a
parameter setting module, a data set to be evaluated; detecting, by
a plurality of data ingestion interfaces, one or more unpermitted
renovation event indicia within the data set, wherein each
interface connects to a unique external data source, and wherein
each interface performs a data mining task process to its data
source to detect the one or more unpermitted renovation event
indicia; identifying, by a renovation detection module, an initial
candidate by applying a machine learning algorithm to unpermitted
renovation event indicia within the data set calculating, by a
renovation probability calculation module, a probability that the
an unpermitted renovation event has taken or is taking place at the
initial candidate accepting, by a validation module, verified data
regarding the unpermitted renovation event; and feeding back the
verified data to the renovation probability calculation module to
improve its prediction over time.
[0036] In some embodiments, the system further comprises
identifying, by the renovation detection module, a plurality of
unpermitted renovation candidates based on the detection indicia
within the data set. In some embodiments, the system further
comprises: a) calculating, by an active renovation probability
calculation module, a probability that each unpermitted renovation
event is an active renovation event; b) accepting, by a
prioritization validation module, verified data regarding the
unpermitted renovation event, active renovation event, or time
until the active renovation event is complete; and c) feeding back
the verified data from b) to the renovation detection calculation
module and active renovation probability calculation module to
improve their prediction over time.
[0037] Another aspect provided herein is a computer-implemented
method for to detecting an unpermitted renovation event and
validating the detected event comprising: defining, by a parameter
setting module, a data set to be evaluated; detecting, by a
plurality of data ingestion interfaces, one or more unpermitted
renovation event indicia within the data set, wherein each
interface connects to a unique external data source, and wherein
each interface performs a data mining task process to its data
source to detect the one or more unpermitted renovation event
indicia; identifying, by a renovation detection module, an initial
candidate by applying a machine learning algorithm to unpermitted
renovation event indicia within the data set calculating, by a
renovation probability calculation module, a probability that the
an unpermitted renovation event has taken or is taking place at the
initial candidate accepting, by a validation module, verified data
regarding the unpermitted renovation event; and feeding back the
verified data to the renovation probability calculation module to
improve its prediction over time. In some embodiments, the method
further comprises identifying, from the renovation detection
module, a plurality of unpermitted renovation candidates based on
the detection indicia within the data set.
[0038] In some embodiments, the method further comprises: a)
calculating, by an active renovation probability calculation
module, a probability that each unpermitted renovation event is an
active renovation event; b) accepting, by a prioritization
validation module, verified data regarding the unpermitted
renovation event, active renovation event, or time until the active
renovation event is complete; and c) feeding back the verified data
from b) to the renovation detection calculation module and active
renovation probability calculation module to improve their
prediction over time.
[0039] Also, in some instances, a technological tool to improve
graphic user interfaces to effectively display reported events and
unreported events is needed. One aspect, disclosed herein, is a
non-transitory computer-readable storage media encoded with a
computer program including instructions executable by a processor
to create a real estate event timeline application, the application
comprising: (i) a reported timeline module configured to a provide
a first timeline and at least one reported event node, wherein the
timeline comprises information of reported real estate events; (ii)
an unreported timeline module configured to provide a second
timeline and at least one unreported event node, wherein the
timeline comprises information of unreported real estate events;
and (iii) a comparative timeline module configured to provide the
first timeline and second timeline, wherein the first timeline and
second timeline are linked so that scrolling moves them both
simultaneously.
BRIEF DESCRIPTION OF THE DRAWINGS
[0040] The novel features of the disclosure are set forth with
particularity in the appended claims. A better understanding of the
features and advantages of the present disclosure will be obtained
by reference to the following detailed description that sets forth
illustrative embodiments, in which the principles of the disclosure
are utilized, and the accompanying drawings of which:
[0041] FIG. 1A is a non-limiting example of a schematic diagram; in
this case, a first exemplary application to detect an unpermitted
renovation event and validate the detected event, in accordance
with some embodiments;
[0042] FIG. 1B is a non-limiting example of a schematic diagram; in
this case, a second exemplary application to detect an unpermitted
renovation event and validate the detected event, in accordance
with some embodiments;
[0043] FIG. 2 is a non-limiting example of a schematic diagram; in
this case, an exemplary process to identify an initial candidate,
in accordance with some embodiments;
[0044] FIG. 3 is a non-limiting example of a schematic diagram; in
this case, an exemplary process to calculate a probability that an
unpermitted renovation event has taken place;
[0045] FIG. 4 shows a non-limiting example of a schematic diagram
of a digital processing device; in this case, a device with one or
more CPUs, a memory, a communication interface, and a display, in
accordance with some embodiments;
[0046] FIG. 5 shows a non-limiting example of a schematic diagram
of a web/mobile application provision system; in this case, a
system providing browser-based and/or native mobile user
interfaces, in accordance with some embodiments;
[0047] FIG. 6 shows a non-limiting example of a schematic diagram
of a cloud-based web/mobile application provision system; in this
case, a system comprising an elastically load balanced,
auto-scaling web server and application server resources as well
synchronously replicated databases, in accordance with some
embodiments;
[0048] FIG. 7 is a non-limiting example of a schematic diagram; in
this case, an exemplary application to detection an unpermitted
renovation event and validate the detected event, in accordance
with some embodiments;
[0049] FIG. 8 is a non-limiting example of a schematic diagram; in
this case, an exemplary application to assign unpermitted
renovation visit to inspectors after receiving a candidate and
validating the assignment, in accordance with some embodiments;
[0050] FIG. 9 is a non-limiting example of a schematic diagram; in
this case, an exemplary application to prioritize inspection of
unpermitted renovation candidates and validate the prioritization,
in accordance with some embodiments;
[0051] FIG. 10 is a non-limiting example of a schematic diagram; in
this case, an exemplary application to prioritize inspection of
unpermitted renovation candidates, in accordance with some
embodiments;
[0052] FIG. 11 is a non-limiting example of a schematic diagram; in
this case, an exemplary application to detect an improper real
estate transfer event, in accordance with some embodiments;
[0053] FIG. 12 is a non-limiting example of a schematic diagram; in
this case, an exemplary application to determine when one or more
unpermitted renovation events has taken place to an unpermitted
renovation candidate, in accordance with some embodiments;
[0054] FIG. 13 is a non-limiting example of a schematic diagram; in
this case, an exemplary application to detect an improper residency
status for a real estate property, in accordance with some
embodiments;
[0055] FIG. 14 is a non-limiting example of a schematic diagram; in
this case, an exemplary application to detect an improper occupancy
tax status for a real estate property, in accordance with some
embodiments;
[0056] FIG. 15 is a non-limiting example of a graphic user
interface; in this case, an interface for viewing publicly
available along with opaque unreported events throughout a
property's existence;
[0057] FIG. 16 is a non-limiting example of a graphic user
interface on a laptop; in this case, an interface for viewing
publicly available along with opaque unreported events throughout a
property's existence;
[0058] FIG. 17 is a non-limiting example of a graphic user
interface on a desktop; in this case, an interface for viewing a
timeline and overview of publicly available events throughout a
property's existence;
[0059] FIG. 18 is a non-limiting example of a graphic user
interface; in this case, an interface for viewing a timeline and
overview of publicly available events throughout a property's
existence;
[0060] FIG. 19 is a non-limiting example of a graphic user
interface; in this case, an interface for viewing a timeline and
overview of publicly available events throughout a property's
existence;
[0061] FIG. 20 is a non-limiting example of a graphic user
interface; in this case, an interface for viewing a timeline and
overview of opaque unreported events throughout a property's
existence;
[0062] FIG. 21 is a non-limiting example of a graphic user
interface; in this case, an interface for simultaneously viewing a
timeline of publicly available along with opaque unreported events
throughout a property's existence;
[0063] FIG. 22 is a non-limiting example of a graphic user
interface; in this case, an interface for viewing and sorting
records associated with a property of interest;
[0064] FIG. 23 is a non-limiting example of a graphic user
interface; in this case, an interface for viewing images of the
property interest; and
[0065] FIG. 24 is a non-limiting example of a graphic user
interface; in this case, a module for toggling the timeline
view.
DETAILED DESCRIPTION OF THE INVENTION
[0066] Application to Detect an Unpermitted Renovation Event and
Validate the Detected Event
[0067] Described herein, in certain embodiments, are non-transitory
computer-readable storage media encoded with a computer program
including instructions executable by a processor to create an
application to detect an unpermitted renovation event and validate
the detected event.
[0068] FIG. 1A is a non-limiting example of a schematic diagram; in
this case, a first exemplary application to detect an unpermitted
renovation event and validate the detected event. FIG. 1 depicts an
example environment 100A that can be employed to execute
embodiments of the present disclosure. The example system 100A
includes computing devices 102, 104, 106, 108, a back-end system
130, and a network 110. In some embodiments, the network 110
includes a local area network (LAN), wide area network (WAN), the
Internet, or a combination thereof, and connects web sites, devices
(e.g., the computing devices 102, 104, 106, 108) and back-end
systems (e.g., the back-end system 130). In some embodiments, the
network 110 can be accessed over a wired and/or a wireless
communications link. For example, mobile computing devices (e.g.,
the smartphone device 102 and the tablet device 106), can use a
cellular network to access the network 110. In some examples, the
users 122-126 may be working as agents for one of the participating
clients in the consortium, such as described above. In some
examples, the users 122-126 may be working as agents for different
clients in the consortium.
[0069] In the depicted example, the back-end system 130 includes at
least one server system 132 and a data store 134. In some
embodiments, the at least one server system 132 hosts one or more
computer-implemented services employed within the described system,
such as XYZ, that users 122-126 can interact with using the
respective computing devices 102-106. For example, the computing
devices 102-106 may be used by respective users 122-126 XYZ through
services hosted by the back-end system 130. In some embodiments,
the back-end system 130 provides an application programming
interface (API) services with which the server computing device 108
may communicate.
[0070] In some embodiments, back-end system 130 may include
server-class hardware type devices. In some embodiments, back-end
system 130 includes computer systems using clustered computers and
components to act as a single pool of seamless resources when
accessed through the network 110. For example, such embodiments may
be used in data center, cloud computing, storage area network
(SAN), and network attached storage (NAS) applications. In some
embodiments, back-end system 130 is deployed using a virtual
machine(s).
[0071] The computing devices 102, 104, 106 may each include any
appropriate type of computing device such as a desktop computer, a
laptop computer, a handheld computer, a tablet computer, a personal
digital assistant (PDA), a cellular telephone, a network appliance,
a camera, a smart phone, an enhanced general packet radio service
(EGPRS) mobile phone, a media player, a navigation device, an email
device, a game console, or an appropriate combination of any two or
more of these devices or other data processing devices. In the
depicted example, the computing device 102 is a smartphone, the
computing device 104 is a desktop computing device, and the
computing device 106 is a tablet-computing device. The server
computing device 108 may include any appropriate type of computing
device, such as described above for computing devices 102-106 as
well as computing devices with server-class hardware. In some
embodiments, the server computing device 108 may include computer
systems using clustered computers and components to act as a single
pool of seamless resources. It is contemplated, however, that
embodiments of the present disclosure can be realized with any of
the appropriate computing devices, such as those mentioned
previously.
[0072] FIG. 1B is a schematic diagram; in this case, an exemplary
application to detect an unpermitted renovation event and validate
the detected event, in accordance with some embodiments. As seen in
FIG. 1B, the exemplary schematic diagram of an exemplary
application to detect an unpermitted renovation event and validate
the detected event 100 comprises: a database 101; an external data
source 102 comprising city records 102a, MLS listings 102b, social
listings 102c, and additional sources 102z; a renovation detection
module 103; a candidate identification module 104; a renovation
probability module 105; and a candidate validation module 106.
Alternatively, the elements of FIG. 1B delineate a schematic
diagram of an exemplary system, method, and a platform.
[0073] Per FIG. 1B, the renovation detection module 103 is
configured to receive a data set from the database 101, and to
receive data from an external data source 102. Optionally, in some
embodiments, the external data source 102 comprises city records
102a, MLS listings 102b, social listings 102c, and additional
sources 102z. Optionally, in some embodiments, the external data
source 102 comprises at least one of city records 102a, MLS
listings 102b, and social listings 102c.
[0074] Optionally, in some embodiments, the data set from the
external data source 102 is defined by at least one of a street
address, a parcel, a street, a lot, a zip code, a county, a state,
an area drawn on a map, an area within a set radial distance from a
location, coordinates set by one or more satellites, an area within
a set driving distance of a location, a GPS point, and an area
defined by at least three GPS points. Optionally, in some
embodiments, the external data source 102 comprises city property
records, county property records, city permit records, county
permit records, post office address database, state business
records, historical real estate listings, rental listings,
demolition orders, dumpster orders, portable restroom orders,
customer account information from third party companies, social
media, phone records, address records, historical credit card
history purchase records, satellite images, tax records, street
views, online photographs, online videos, signs outside a property,
or the Internet. Optionally, in some embodiments, rental listings
can include AirBnB or Craigslist.
[0075] Optionally, in some embodiments, the renovation detection
module 103 comprises a plurality of data ingestion interfaces, each
interface connecting to one external data source 102. Optionally,
in some embodiments, the renovation detection module 103 comprises
a plurality of data ingestion interfaces comprising at least one of
a city records data ingestion interface, an MLS listings data
ingestion interface, and a social listings data ingestion
interface. Optionally, in some embodiments, each interface is
configured to perform at least one of a natural language task
process and a computer vision task process to its data source.
Optionally, in some embodiments, each interface is configured to
detect one or more unpermitted renovation event indicia within the
data set from the external data source 102. Optionally, in some
embodiments, each interface is configured to perform a data mining
task process to its data source to detect one or more unpermitted
renovation event indicia within the data set. Optionally, in some
embodiments, the data mining process comprises a natural language
task process, numerical data mining task process, or a photographic
data mining task process.
[0076] Optionally, in some embodiments, the natural language task
process comprises syntax interpretation, semantic interpretation,
discourse interpretation, or speech interpretation. Optionally, in
some embodiments, the syntax interpretation comprises
lemmatization, morphological segmentation, part-of-speech tagging,
parsing, sentence boundary disambiguation, stemming, word
segmentation, or terminology extraction. Optionally, in some
embodiments, the semantic interpretation comprises lexical
semantics, machine translation, named entity recognition, natural
language generation, natural language understanding, optical
character recognition, question answering, recognizing textual
entailment, relationship extraction, sentiment analysis, topic
segmentation, or word sense disambiguation. Optionally, in some
embodiments, the discourse interpretation comprises automatic
summarization, coreference resolution, or discourse analysis.
Optionally, in some embodiments, the speech interpretation
comprises speech recognition, speech segmentation, and
text-to-speech. Optionally, in some embodiments, the computer
vision task process comprises analysis, object recognition, object
identification, object detection, content-based image retrieval,
optical character recognition, facial recognition, shape
recognition, egomotion, object tracking, optical flow, or any
combination thereof. In some embodiments, the natural language task
process model employs word-level features, n-gram features, or
both. The word-level features can be gleaned from textual
descriptions. The textual descriptions can comprise stored property
descriptions, headlines, property features, or any combination
thereof. In some embodiments, the natural language task process
model structures the textual descriptions. In some embodiments, the
natural language task process model then presents the structured
textual descriptions for model analysis. The model analysis can
then rank the importance one or more of the structured textual
descriptions by assessing their prevalence in the target data. In
some cases the model analysis ignores one or more textual
descriptions. In some cases the model analysis does not discard any
textual descriptions.
[0077] Optionally, in some embodiments, the unpermitted renovation
event comprises violations of building codes, past unpermitted
renovations, present unpermitted renovations, additions to a
property, upgrades to a property, or modifications to a
property.
[0078] Optionally, in some embodiments, the renovation detection
module 103 applies a machine learning algorithm to identify an
initial candidate property based on the detection indicia within
the data set from the external data source 102.
[0079] Optionally, in some embodiments, the detection of one or
more unpermitted renovation event indicia comprises determining a
square footage of a property, a change in the square footage of a
property, a bed count of a property, a change in a bed count of a
property, a bathroom count of a property, a change in a bathroom
count of a property, a valuation of a property, a change in a
valuation of the property, ownership of a property, a corporation
owning a property, an owner with a history of flipping one or more
properties, lenders on a property, a renovation scale, or liens on
a property.
[0080] Per FIG. 1, the candidate identification module 104 can
receive the initial candidate from the renovation detection module
103, identifies a candidate property, and send the candidate
property to the renovation probability module 105. Optionally, in
some embodiments, the renovation probability module 105 calculates
a probability that an unpermitted renovation event has taken or is
taking place at the candidate property. If the probability that an
unpermitted renovation event has taken or is taking place at the
candidate property is above a set threshold, the renovation
probability module 105 can send at least one of the candidate and
the probability to the candidate validation module 106.
[0081] Optionally, in some embodiments, the calculation comprises
applying an increased weighted factor that the unpermitted
renovation event has taken place if a property is owned by a
corporation. Optionally, in some embodiments, the calculation
comprises applying an increased weighted factor that the
unpermitted renovation event has taken place if one or more
corporate officers have previously flipped properties. Optionally,
in some embodiments, the calculation comprises applying an
increased weighted factor that the unpermitted renovation event has
taken place if a property owner's social media displays
renovations. Optionally, in some embodiments, the calculation
comprises applying an increased weighted factor that the
unpermitted renovation event has taken place if a real estate
listing displays renovations. Optionally, in some embodiments, the
calculation comprises calculating whether a probability threshold
has been met.
[0082] Optionally, in some embodiments, the candidate validation
module 106 receives at least one of the candidate and the
probability to the candidate validation module, and a verified data
107 regarding the unpermitted renovation event. Per FIG. 1, the
candidate validation module 106 can then feed back the verified
data 107 to at least one of the renovation probability calculation
module 105 and the renovation detection module 103 to improve its
prediction over time. In some embodiments, the renovation detection
module stores these calculations to improve predictions in the
database 101.
[0083] Optionally, in some embodiments, the verified data 107 is
acquired by a public official inspecting a candidate property.
Optionally, in some embodiments, the verified data 107 is an issued
permit for the renovated event at the initial candidate.
Optionally, in some embodiments, the media further comprises a
secondary screening module, wherein if the renovation probability
module 105 calculates a probability in excess of a predetermined
threshold, the secondary screening module proceeds to conduct
further screening procedures.
[0084] FIG. 2 is a schematic diagram; in this case, an exemplary
process to identify an initial candidate, in accordance with some
embodiments. As seen in FIG. 2, an initial candidate can be
identified, wherein the renovation probability module 200 receives
an initial candidate 201 from the initial candidate identification
module, whereby the renovation probability module 200 determines
whether or not the owner of the initial candidate property is a
corporation 202. Optionally, in some embodiments, if the owner is a
corporation, the probability that the renovation is a flip 203 is
increased. Optionally, in some embodiments, if the owner is not a
corporation, the probability that the renovation is a flip is not
increased.
[0085] The renovation probability module 200 can then determine
whether or not the owner of the initial candidate property has
previously flipped a property 204. Optionally, in some embodiments,
if the owner of the initial candidate property has previously
flipped a property, the probability that the renovation is a flip
is increased 205. Optionally, in some embodiments, if the owner of
the initial candidate property has not previously flipped a
property, the probability that the renovation is a flip is not
increased. In some cases the probability that the renovation is a
flip is increased 203 205 by a set probability value. In some cases
the probability that the renovation is a flip is increased 203 205
by a variable probability value. Optionally, in other embodiments,
the renovation probability module determines whether or not the
owner of the initial candidate property has previously performed
any number of unpermitted renovation act to a property. Optionally,
in further embodiments, the renovation probability module performs
the aforementioned steps for the unpermitted renovation act.
[0086] Per FIG. 2, the renovation probability module 200 can
determine if a probability that the renovation is a flip reaches a
certain threshold. Optionally, in some embodiments, if the
probability threshold is met, the renovation probability module 200
recommends the initial candidate for further screening 207.
Alternatively, if the probability threshold is not met, the
probability module 200 can recommend that the initial candidate be
ignored 208. Optionally, in some embodiments, if the probability
threshold is not met, the probability module prioritizes candidates
higher than others based on this stage and all properties will be
fed into the next module. Optionally, in some embodiments,
probability module 200 is configured to send the recommendation
that the initial candidate requires further screening 207 to the
candidate validation module.
[0087] Optionally, in some embodiments, the renovation probability
module 200 can be further configured to receive a verified data
from the validation module. The verified data can then be used to
adjust the set or variable probability value the renovation is a
flip is increased 203 205 by to improve its prediction over time. A
prediction improvement can ensure that the initial candidates for
further screening 207 require further screening, the initial
candidate that are ignored 208 should be ignored, or both.
[0088] Optionally, in some embodiments, the unpermitted renovation
event comprises violations of building codes, past unpermitted
renovations, present unpermitted renovations, additions to a
property, upgrades to a property, or modifications to a property.
Optionally, in some embodiments, the verified data is acquired by a
public official inspecting an initial candidate property.
Optionally, in some embodiments, the verified data is an issued
permit for the renovated event at the initial candidate.
Optionally, in some embodiments, the media further comprises a
secondary screening module, wherein if the probability calculation
module calculates a probability in excess of a predetermined
threshold, the secondary screening module proceeds to conduct
further screening procedures.
[0089] FIG. 3 is a schematic diagram; in this case, an exemplary
process to calculate a probability that an unpermitted renovation
event has taken place. As seen in FIG. 3, the renovation
probability module 300 can calculate a probability that an
unpermitted renovation event has taken place by receiving a
candidate for further screening 201 from the renovation probability
module, whereby the renovation probability module 300 determines
whether or not the owner of the candidate property is a corporation
310. Optionally, in some embodiments, if the owner of the candidate
property is not a corporation, or if the owner is an individual,
the renovation probability module 300 checks the individual's
social media 311. Alternatively, if the owner of the candidate
property is a corporation, or is not an individual, the renovation
probability module 300 determines the officer or officers of the
corporation 312 and checks the officer's or officers' social media
313. Optionally, in some embodiments, if either the individual's
social media 311 or the officer social media 313 displays
renovations 320, then the probability of a flip is increased 340.
Additionally, if the owner of the candidate property is a
corporation, or is not an individual, the renovation probability
module 300 can determine whether or not the officer of the
corporation have previously flipped a property 330, and increase
the probability of a flip 340 if such evidence is found.
[0090] Additionally, in series or in parallel, the renovation
probability module 300 can check MLS listings and other sources 302
to determine whether renovations are displayed 330, whereby the
probability of a flip is increased 340 if such evidence is
found.
[0091] Additionally, the renovation probability module 300 can then
determine whether or not the probability of a flip is greater than
a T2 threshold 350. Optionally, in some embodiments, the T2
threshold comprises a set threshold or a variable threshold,
whereby flip probabilities above the T2 value are highly indicative
of a flip, and potential unpermitted renovations associated with
the flip. Per FIG. 3, the renovation probability module 300 submits
an instruction to inspect the candidate property 370 if the
probability of a flip is greater than the T2 threshold.
Alternatively, if the probability of a flip is less than the T2
threshold, the renovation probability module 300 determines whether
or not the probability of a flip is greater than a T3 threshold
360. Optionally, in some embodiments, the T3 threshold comprises a
set threshold or a variable threshold, whereby flip probabilities
above the T3 value are moderately indicative of a flip and require
further evidence and/or analysis to increase the certainty of a
flip before inspection, and whereby T3 represents a lower
probability than T2. Per FIG. 3, the renovation probability module
300 submits an instruction to ignore the candidate property 380 if
the probability of a flip is less than the T3 threshold.
Alternatively, if the probability of a flip is greater than the T3
threshold (and less than the T2 threshold 350) the renovation
probability module 300 performs further research and analysis by
rechecking the individual's or corporation's social media 311 313
and checking MLS and other sources 302.
[0092] Optionally, in some embodiments, the renovation probability
module 300 is further configured to feed back the verified data to
the renovation probability calculation module to improve its
prediction over time.
[0093] FIG. 7 is an exemplary schematic diagram of an exemplary
application to detect an unpermitted renovation event and validate
the detected event, in accordance with some embodiments. As seen in
FIG. 7, the exemplary schematic diagram of an exemplary application
to detect an unpermitted renovation event and validate the detected
event 700 comprises: a database 701; an external data source 702
comprising city records 702a, MLS listings 702b, social listings
702c, and additional sources 702z; a machine learning and filtering
engine 703; a recommended action to send an inspector 704; and a
confirmation of a correct/incorrect recommendation 706.
[0094] Per FIG. 7, the machine learning and filtering engine 703 is
configured to receive a data set from the database 701, and to
receive data from an external data source 702. Optionally, in some
embodiments, the external data source 702 comprises city records
702a, MLS listings 702b, social listings 702c, and additional
sources 702z. Optionally, in some embodiments, the external data
source 702 comprises at least one of city records 702a, MLS
listings 702b, and social listings 702c.
[0095] Optionally, in some embodiments, the data set from the
external data source 702 is defined by at least one of a street
address, a parcel, a street, a lot, a zip code, a county, a state,
an area drawn on a map, an area within a set radial distance from a
location, coordinates set by one or more satellites, an area within
a set driving distance of a location, a GPS point, and an area
defined by at least three GPS points. Optionally, in some
embodiments, the external data source 702 comprises city property
records, county property records, city permit records, county
permit records, post office address database, state business
records, historical real estate listings, rental listings,
demolition orders, dumpster orders, portable restroom orders,
customer account information from third party companies, social
media, phone records, address records, historical credit card
history purchase records, satellite images, tax records, street
views, online photographs, online videos, signs outside a property,
or the Internet. Optionally, in some embodiments, rental listings
can include AirBnB or Craigslist.
[0096] Optionally, in some embodiments, the machine learning and
filtering engine 703 comprises a plurality of data ingestion
interfaces, each interface connecting to one external data source
702. Optionally, in some embodiments, the machine learning and
filtering engine 703 comprises a plurality of data ingestion
interfaces comprising at least one of a city records data ingestion
interface, an MLS listings data ingestion interface, and a social
listings data ingestion interface. Optionally, in some embodiments,
each interface is configured to perform at least one of a natural
language task process and a computer vision task process on its
data source. Optionally, in some embodiments, each interface is
configured to detect one or more unpermitted renovation event
indicia within the data set from the external data source 702.
Optionally, in some embodiments, each interface is configured to
perform a data mining task process to its data source to detect one
or more unpermitted renovation event indicia within the data set.
Optionally, in some embodiments, the data mining process comprises
a natural language task process, numerical data mining task
process, or a photographic data mining task process.
[0097] Optionally, in some embodiments, the natural language task
process comprises syntax interpretation, semantic interpretation,
discourse interpretation, or speech interpretation. Optionally, in
some embodiments, the syntax interpretation comprises
lemmatization, morphological segmentation, part-of-speech tagging,
parsing, sentence boundary disambiguation, stemming, word
segmentation, or terminology extraction. Optionally, in some
embodiments, the semantic interpretation comprises lexical
semantics, machine translation, named entity recognition, natural
language generation, natural language understanding, optical
character recognition, question answering, recognizing textual
entailment, relationship extraction, sentiment analysis, topic
segmentation, or word sense disambiguation. Optionally, in some
embodiments, the discourse interpretation comprises automatic
summarization, coreference resolution, or discourse analysis.
Optionally, in some embodiments, the speech interpretation
comprises speech recognition, speech segmentation, and
text-to-speech. Optionally, in some embodiments, the computer
vision task process comprises object recognition, object
identification, object detection, content-based image retrieval,
optical character recognition, facial recognition, shape
recognition, egomotion, object tracking, optical flow, or any
combination thereof.
[0098] Optionally, in some embodiments, the unpermitted renovation
event comprises violations of building codes, past unpermitted
renovations, present unpermitted renovations, additions to a
property, upgrades to a property, or modifications to a
property.
[0099] Optionally, in some embodiments, the machine learning and
filtering engine 703 applies a machine learning algorithm to
identify an initial candidate property based on the detection
indicia within the data set from the external data source 702.
[0100] Optionally, in some embodiments, the detection of one or
more unpermitted renovation event indicia comprises determining a
square footage of a property, a change in the square footage of a
property, a bed count of a property, a change in a bed count of a
property, a bathroom count of a property, a change in a bathroom
count of a property, a valuation of a property, a change in a
valuation of the property, ownership of a property, a corporation
owning a property, an owner with a history of flipping one or more
properties, lenders on a property, a renovation scale, or liens on
a property.
[0101] Per FIG. 7, the recommended action to send an inspector 704
can be sent by the machine learning and filtering engine 703,
whereby the confirmation of correct/incorrect recommendation 706 is
then initiated. Optionally, in some embodiments, the machine
learning and filtering engine 703 calculates a probability that an
unpermitted renovation event has taken or is taking place at the
candidate property. If the probability that an unpermitted
renovation event has taken or is taking place at the candidate
property is above a set threshold, the machine learning and
filtering engine 703 sends an instruction for the confirmation of
correct/incorrect recommendation 706.
[0102] Optionally, in some embodiments, the calculation comprises
applying an increased weighted factor that the unpermitted
renovation event has taken place if a property is owned by a
corporation. Optionally, in some embodiments, the calculation
comprises applying an increased weighted factor that the
unpermitted renovation event has taken place if one or more
corporate officers have previously flipped properties. Optionally,
in some embodiments, the calculation comprises applying an
increased weighted factor that the unpermitted renovation event has
taken place if a property owner's social media displays
renovations. Optionally, in some embodiments, the calculation
comprises applying an increased weighted factor that the
unpermitted renovation event has taken place if a real estate
listing displays renovations. Optionally, in some embodiments, the
calculation comprises calculating whether a probability threshold
has been met.
[0103] Optionally, in some embodiments, the confirmation of a
correct/incorrect recommendation 706 is initiated by at least one
of the candidate and the probability to the candidate validation
module, and a verified data 707 regarding the unpermitted
renovation event. Per FIG. 7, the confirmation of a
correct/incorrect recommendation 706 can then feed back the
verified data 707 to at least one of the renovation probability
calculation module 703 and the machine learning and filtering
engine 703, based on whether or not the correct or incorrect
recommendation is provided, to improve its prediction over
time.
[0104] Optionally, in some embodiments, the verified data 707 is
acquired by a public official inspecting a candidate property.
Optionally, in some embodiments, the verified data 707 is an issued
permit for the renovated event at the initial candidate.
Optionally, in some embodiments, the media further comprises a
secondary screening module, wherein if the renovation probability
module 705 calculates a probability in excess of a predetermined
threshold, the secondary screening module proceeds to conduct
further screening procedures.
[0105] Another aspect disclosed herein is a computer-implemented
method of training a neural network for detection of an unpermitted
renovation event, the method comprising: collecting a data from a
data source by a plurality of data ingestion interfaces; applying
one or more data mining task processes to the data from a data
source to determine one or more digital unpermitted renovation
event indicia; creating a first training set comprising the
collected data from a data source, the digital unpermitted
renovation event indicia, and a set of digital permitted renovation
event indicia; training the neural network in a first stage using
the first training set; creating a second training set for a second
stage of training comprising the first training set and digital
permitted renovation event indicia that are incorrectly detected as
unpermitted renovations after the first stage of training; and
training the neural network in a second stage using the second
training set.
[0106] Another aspect provided herein is a computer-implemented
method of training a neural network for detection of an unpermitted
renovation event, the method comprising: collecting a data from a
data source by a plurality of data ingestion interfaces; applying
one or more data mining task processes to the data from a data
source to determine one or more digital unpermitted renovation
event indicia; determining an initial candidate from the data from
a data source based on the digital unpermitted event indicia;
determining a probability that an unpermitted renovation event has
taken or is taking place at the initial candidate creating a first
training set comprising the collected digital unpermitted
renovation event indicia, the determined probability that the
unpermitted renovation event has taken or is taking place at the
initial candidate and a set of digital permitted renovation event
indicia; training the neural network in a first stage using the
first training set; creating a second training set for a second
stage of training comprising the first training set and the digital
permitted renovation event indicia that are detected to have a set
minimum probability that the unpermitted renovation event has taken
or is taking place at the initial candidate, after the first stage
of training; and training the neural network in a second stage
using the second training set.
[0107] Another aspect provided herein is a computer-implemented
method of training a neural network for detection of an active
unpermitted renovation event, the method comprising: collecting a
data from a data source by a plurality of data ingestion
interfaces; applying one or more data mining task processes to the
data from a data source to determine one or more digital active
unpermitted renovation event indicia; determining an initial
candidate from the data from a data source based on the digital
active unpermitted event indicia; determining a probability that an
active unpermitted renovation event has taken or is taking place at
the initial candidate creating a first training set comprising the
collected digital active unpermitted renovation event indicia, the
determined probability that the active unpermitted renovation event
has taken or is taking place at the initial candidate and a set of
digital permitted renovation event indicia; training the neural
network in a first stage using the first training set; creating a
second training set for a second stage of training comprising the
first training set and the digital permitted renovation event
indicia that are detected to have a set minimum probability that
the active unpermitted renovation event has taken or is taking
place at the initial candidate, after the first stage of training;
and training the neural network in a second stage using the second
training set.
[0108] Another aspect disclosed herein is a computer-implemented
method of training a neural network for detection of an improper
real estate transfer, the method comprising: collecting a data from
a data source by a plurality of data ingestion interfaces; applying
one or more data mining task processes to the data from a data
source to determine one or more digital improper real estate
transfer indicia; creating a first training set comprising the
collected data from a data source, the digital improper real estate
transfer indicia, and a set of digital proper real estate transfer
indicia; training the neural network in a first stage using the
first training set; creating a second training set for a second
stage of training comprising the first training set and digital
proper real estate transfer indicia that are incorrectly detected
as improper real estate transfers, after the first stage of
training; and training the neural network in a second stage using
the second training set.
[0109] Another aspect provided herein is a computer-implemented
method of training a neural network for detection of an improper
real estate transfer, the method comprising: collecting a data from
a data source by a plurality of data ingestion interfaces; applying
one or more data mining task processes to the data from a data
source to determine one or more digital improper real estate
transfer indicia; determining an initial candidate from the data
from a data source based on the digital improper event indicia;
determining a probability that an improper real estate transfer has
taken or is taking place at the initial candidate creating a first
training set comprising the collected digital improper real estate
transfer indicia, the determined probability that the improper real
estate transfer has taken or is taking place at the initial
candidate and a set of digital proper real estate transfer indicia;
training the neural network in a first stage using the first
training set; creating a second training set for a second stage of
training comprising the first training set and the digital proper
real estate transfer indicia that are detected to have a set
minimum probability that the improper real estate transfer has
taken or is taking place at the initial candidate, after the first
stage of training; and training the neural network in a second
stage using the second training set.
[0110] Another aspect provided herein is a computer-implemented
method of training a neural network for detection of an active
improper real estate transfer, the method comprising: collecting a
data from a data source by a plurality of data ingestion
interfaces; applying one or more data mining task processes to the
data from a data source to determine one or more digital active
improper real estate transfer indicia; determining an initial
candidate from the data from a data source based on the digital
active improper event indicia; determining a probability that an
active improper real estate transfer has taken or is taking place
at the initial candidate creating a first training set comprising
the collected digital active improper real estate transfer indicia,
the determined probability that the active improper real estate
transfer has taken or is taking place at the initial candidate and
a set of digital proper real estate transfer indicia; training the
neural network in a first stage using the first training set;
creating a second training set for a second stage of training
comprising the first training set and the digital proper real
estate transfer indicia that are detected to have a set minimum
probability that the active improper real estate transfer has taken
or is taking place at the initial candidate, after the first stage
of training; and training the neural network in a second stage
using the second training set.
[0111] Another aspect provided herein is a computer-implemented
method of training a neural network for detection of an active
unpermitted renovation event, the method comprising: collecting a
data from a data source by a plurality of data ingestion
interfaces; applying one or more data mining task processes to the
data from a data source to determine one or more digital active
unpermitted renovation event indicia; determining an initial
candidate from the data from a data source based on the digital
active unpermitted event indicia; determining an estimated time
range that an active unpermitted renovation event has taken or is
taking place at the initial candidate creating a first training set
comprising the collected digital active unpermitted renovation
event indicia, the estimated time range that the active unpermitted
renovation event has taken or is taking place at the initial
candidate and a set of digital time ranges that an active
unpermitted renovation event has taken place; training the neural
network in a first stage using the first training set; creating a
second training set for a second stage of training comprising the
first training set and the digital permitted renovation event
indicia that are detected to have a set minimum estimated time
range that the active unpermitted renovation event has taken or is
taking place at the initial candidate, after the first stage of
training; and training the neural network in a second stage using
the second training set.
[0112] Another aspect disclosed herein is a computer-implemented
method of training a neural network for detection of an improper
residency status, the method comprising: collecting a data from a
data source by a plurality of data ingestion interfaces; applying
one or more data mining task processes to the data from a data
source to determine one or more digital improper residency status
indicia; creating a first training set comprising the collected
data from a data source, the digital improper residency status
indicia, and a set of digital proper residency status indicia;
training the neural network in a first stage using the first
training set; creating a second training set for a second stage of
training comprising the first training set and digital proper
residency status indicia that are incorrectly detected as improper
residencies after the first stage of training; and training the
neural network in a second stage using the second training set.
[0113] Another aspect provided herein is a computer-implemented
method of training a neural network for detection of an improper
residency status, the method comprising: collecting a data from a
data source by a plurality of data ingestion interfaces; applying
one or more data mining task processes to the data from a data
source to determine one or more digital improper residency status
indicia; determining an initial candidate from the data from a data
source based on the digital improper event indicia; determining a
probability that an improper residency status has taken or is
taking place at the initial candidate creating a first training set
comprising the collected digital improper residency status indicia,
the determined probability that the improper residency status has
taken or is taking place at the initial candidate and a set of
digital proper residency status indicia; training the neural
network in a first stage using the first training set; creating a
second training set for a second stage of training comprising the
first training set and the digital proper residency status indicia
that are detected to have a set minimum probability that the
improper residency status has taken or is taking place at the
initial candidate, after the first stage of training; and training
the neural network in a second stage using the second training
set.
[0114] Another aspect provided herein is a computer-implemented
method of training a neural network for detection of an active
improper residency status, the method comprising: collecting a data
from a data source by a plurality of data ingestion interfaces;
applying one or more data mining task processes to the data from a
data source to determine one or more digital active improper
residency status indicia; determining an initial candidate from the
data from a data source based on the digital active improper event
indicia; determining a probability that an active improper
residency status has taken or is taking place at the initial
candidate creating a first training set comprising the collected
digital active improper residency status indicia, the determined
probability that the active improper residency status has taken or
is taking place at the initial candidate and a set of digital
proper residency status indicia; training the neural network in a
first stage using the first training set; creating a second
training set for a second stage of training comprising the first
training set and the digital proper residency status indicia that
are detected to have a set minimum probability that the active
improper residency status has taken or is taking place at the
initial candidate, after the first stage of training; and training
the neural network in a second stage using the second training
set.
[0115] Another aspect disclosed herein is a computer-implemented
method of training a neural network for detection of an improper
tax status, the method comprising: collecting a data from a data
source by a plurality of data ingestion interfaces; applying one or
more data mining task processes to the data from a data source to
determine one or more digital improper tax status indicia; creating
a first training set comprising the collected data from a data
source, the digital improper tax status indicia, and a set of
digital proper tax status indicia; training the neural network in a
first stage using the first training set; creating a second
training set for a second stage of training comprising the first
training set and digital proper tax status indicia that are
incorrectly detected as improper tax status after the first stage
of training; and training the neural network in a second stage
using the second training set.
[0116] Another aspect provided herein is a computer-implemented
method of training a neural network for detection of an improper
tax status, the method comprising: collecting a data from a data
source by a plurality of data ingestion interfaces; applying one or
more data mining task processes to the data from a data source to
determine one or more digital improper tax status indicia;
determining an initial candidate from the data from a data source
based on the digital improper event indicia; determining a
probability that an improper tax status has taken or is taking
place at the initial candidate creating a first training set
comprising the collected digital improper tax status indicia, the
determined probability that the improper tax status has taken or is
taking place at the initial candidate and a set of digital proper
tax status indicia; training the neural network in a first stage
using the first training set; creating a second training set for a
second stage of training comprising the first training set and the
digital proper tax status indicia that are detected to have a set
minimum probability that the improper tax status has taken or is
taking place at the initial candidate, after the first stage of
training; and training the neural network in a second stage using
the second training set.
[0117] Another aspect provided herein is a computer-implemented
method of training a neural network for detection of an active
improper tax status, the method comprising: collecting a data from
a data source by a plurality of data ingestion interfaces; applying
one or more data mining task processes to the data from a data
source to determine one or more digital active improper tax status
indicia; determining an initial candidate from the data from a data
source based on the digital active improper event indicia;
determining a probability that an active improper tax status has
taken or is taking place at the initial candidate creating a first
training set comprising the collected digital active improper tax
status indicia, the determined probability that the active improper
tax status has taken or is taking place at the initial candidate
and a set of digital proper tax status indicia; training the neural
network in a first stage using the first training set; creating a
second training set for a second stage of training comprising the
first training set and the digital proper tax status indicia that
are detected to have a set minimum probability that the active
improper tax status has taken or is taking place at the initial
candidate, after the first stage of training; and training the
neural network in a second stage using the second training set.
[0118] At least one of the first stage of training and the second
stage of training can employ a similarity metric to find large
datasets which are similar to a small hand-annotated dataset. At
least one of the first stage of training and the second stage of
training can be refined and re-trained using human feedback. In
some embodiments, at least one of the first stage of training and
the second stage of training comprises a distant supervision
method. The distant supervision method can create a large training
set seeded by a small hand-annotated training set. The distant
supervision method can comprise positive-unlabeled learning with
the training set as the `positive` class. The distant supervision
method can employ a logistic regression model, a recurrent neural
network, or both. The recurrent neural network can be advantageous
for Natural Language Processing (NLP) machine learning. In some
embodiments, at least one of the first stage of training and the
second stage of training comprises a human annotated method. The
human annotated method can employ labels can be provided by a
hand-crafted heuristic. For example, the hand-crafted heuristic can
comprise examining differences between public and county records.
The semi-supervised labels can be determined using a clustering
technique to find properties similar to those flagged by previous
human annotated labels and previous semi-supervised labels. The
semi-supervised labels can employ a XGBoost, a neural network, or
both.
[0119] Assigning Unpermitted Renovation Visit to Inspectors
[0120] FIG. 8 is an exemplary schematic diagram of an exemplary
application to assign unpermitted renovation visit to inspectors,
in accordance with some embodiments. As seen in FIG. 8, the
exemplary application to assign unpermitted renovation visit to
inspectors 800 comprises a receiving a candidate 801, calculating a
probability of active renovation event 802, checking social media,
MLS, and other sources 803, determining remaining days until
completion of active renovation event (F) 804, determining
candidate-inspector distances 805, assigning inspectors to
candidate based on (D) and (F) 806, determining inspection results
807, and updating the database 808. Alternatively, the application
to assign unpermitted renovation visit to inspectors 800 comprises
a receiving a candidate 801, calculating a probability of active
renovation event 802, checking social media, MLS, and other sources
803, and determining remaining days until completion of active
renovation event (F) 804. In some embodiments, the application to
assign unpermitted renovation visit to inspectors 800 does not
comprise determining candidate-inspector distances 805, assigning
inspectors to candidate based on (D) and (F) 806, determining
inspection results 807, or updating the database 808.
[0121] As seen in FIG. 8, the application to detect an unpermitted
renovation event at a candidate and validate the detected event can
further comprise an application to assign inspectors to the
candidate 800. In some instances, the authorities who enforce the
various regulations have a limited number of inspectors and other
sources. Hence, in some instances, it can occur that the ability to
find such properties can exceed the ability of the appropriate
authorities to undertake inspection or enforcement action.
Accordingly, in some instances, it is beneficial to prioritize the
list of properties for the authorities to optimize or maximize the
efficiency of inspecting or taking enforcement action depending on
the kind of renovation. Optionally, in some embodiments, it is also
or alternatively beneficial to prioritize the list of active
renovation event by an estimation of the value of the renovation,
amount of dollars spent on the renovation, the potential fee and
penalties to be collected, or impact on property taxes.
[0122] Data mining techniques can be used to identify unpermitted
renovations. In many instances, it is easier for the authority to
act upon an active renovation than a historical one because the
authority can just go to the property to observe the activity as it
is going on. It is particularly advantageous to identify those
properties while the renovation is still in progress. The
inspection assignment applications herein are further configured to
properly assign potential unpermitted properties to inspectors, to
ensure that a maximum quantity and/or quality of potential evidence
can be collected.
[0123] Per FIG. 8, in some embodiments, the application to assign
unpermitted renovation visit to inspectors 800 comprises receiving
a candidate 801, as determined per FIG. 1, 2, or 3. The application
calculates the probability of an active renovation event occurring
at the candidate 802. Optionally, in some embodiments, the
application then checks social media, MLS data, and other sources
803 to determine an estimated remaining number of days until
completion of the active renovation event (F) 804. Optionally, in
some embodiments, the application can further determine the
candidate-inspector distances (D) 805. Optionally, in some
embodiments, the application can further determine the location of
other scheduled inspections for the candidate-inspector.
Optionally, in some embodiments, the application can further
determine the infringement type and the appropriate
candidate-inspector skill. Optionally, in some embodiments, the
application can further determine whether there is the potential to
inspect at an Open House (e.g., inspect indoors and easily access
other areas in the property not normally easily viewable or
accessible from outdoors) if one is scheduled. In further
embodiments, the application can assign inspectors to the candidate
based on the (D) distance from the property and the one or more
candidates and (F) 806. Subsequently, the inspector can determine
inspection results 807, and update the database 808 with any
garnered information. Optionally, in some embodiments, the
application to assign unpermitted renovation visit to inspectors
800 is permitted to run automatically every period of time to
schedule and/or reschedule the property-inspector assignments.
Optionally, in some embodiments, the period of time is equal to,
one minute, thirty minutes, one hour, 12 hours, one day, one week,
one month, or one year.
[0124] Optionally, in some embodiments, the calculation of the
probability of an active renovation event is based on at least one
of the recency of the purchase date, the recency of an unpermitted
renovation indicia, the flip probability, and the determination
that the owner is a corporation. The indicia of active renovations
can comprise a current renovation probability factor that the
predicted unpermitted renovation at the candidate property
comprises an active renovation. Optionally, in some embodiments,
checking social media, MLS data, and other sources 803 garners
further indicia of active renovations. These further indicia can be
used to recalculate the active renovation probability event factor,
and/or to increase confidence in the active renovation event
probability factor. Further, checking social media, MLS data, and
other sources 803 can provide further evidence necessary to
determine an estimated number of days remaining in the renovation
(F) 804. Optionally, in some embodiments, the calculation of the
probability of an active renovation event 802 is further associated
with a determination of the number for remaining renovation days
(F) 804. Checking social media, MLS data, and other sources 803 can
comprise reviewing street or satellite images, determining specific
social media indicia such as the terms "stage," "almost done," or
"halfway there." Optionally, in some embodiments, the estimation is
based on the remaining amount of time until completion of an active
renovation event. Optionally, in some embodiments the active
renovation event is further prioritized by an estimation of the
value of the renovation, since fees and penalties can depend on
this value.
[0125] Such evidence necessary to determine an estimated number of
days or time remaining in the renovation (F) 804 can comprise
evidence of the purchase or use of materials, tools, or services
associated with early or late stages of construction. Materials
associated with early stages of construction can comprise, for
example, wood or concrete, whereby materials associated with later
stages of construction can comprise, for example, paint, plaster,
appliances and fixtures. Tool rentals or purchases associated with
early stages of construction can comprise, for example, demolition
bins and jack hammers, whereby materials associated with later
stages of construction can comprise, for example, paintbrushes, and
tile cutters. Services associated with early stages of construction
can comprise, for example, waste removal and plumbing, whereby
services associated with later stages of construction can comprise,
for example, electrical instillation, and appliance delivery.
[0126] The determination of a property-inspector distance (D) 805
ensures optimal use of the available inspectors. Optionally, in
some embodiments, the property-inspector distance (D) comprises a
distance between the address of the property and the inspector's
home address, the governmental agency's address, a prior inspection
property, or any combination thereof. The (D) value associated with
one inspector can be equal to the (D) value of one or more other
inspectors. The (D) value associated with one inspector can be
unequal to the (D) value of one or more other inspectors.
Optionally, in some embodiments, the prior inspection property
comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, or more inspection
properties.
[0127] To ensure high inspector efficiency, and that the most
amount of candidate properties are inspected during potential
active construction, the inspector is assigned to inspect a
property based on D and F 806. Optionally, in some embodiments, the
inspector is assigned to inspect a property 806 by assigning
properties in order by ascending D values and ascending F values.
Optionally, in some embodiments, the inspector is assigned to
inspect a property 806 by assigning properties in order by
ascending F values and ascending D values. Optionally, in some
embodiments, the D and F are used to calculate an inspection
efficiency parameter (n), wherein a high n value correlates with
inspection urgency and efficacy. Optionally, in some embodiments,
the inspection efficiency parameter is calculated as:
n = 1 D + F ##EQU00001##
[0128] In other embodiments, the inspection efficiency parameter is
calculated as:
n = 1 aD + bD 2 + cF + eF 2 + gDF + h ( DF ) 2 ##EQU00002##
where (a), (b), (c), (e), (g), and (h) are set constants.
Optionally, in some embodiments, at least one of the (a), (b), (c),
(e), (g), and (h) constants are equal to zero. Optionally, in some
embodiments, the (a), (b), (c), (e), (g), and (h) constants are
determined by a machine learning algorithm. The (n) value can be
calculated for each inspector within a plurality of inspectors.
Alternatively, the inspection efficiency parameter can be based on
the logarithm of F, which becomes more important as it approaches
1. As such, the inspection efficiency parameter is calculated
as:
n = 1 D + log ( F - 1 ) ##EQU00003##
[0129] Additional parameters, beyond (D) and (F) can be used to
assign inspectors to properties, such as a parameter associated
with the seniority of the inspector, a parameter associated with
the specific skills of the inspector, a parameter associated with
the inspection history of the inspector, the value of the
renovation, or any combination thereof.
[0130] Further, the inspector is assigned to inspect a property
based on D and F 806 to maximize the (n) value among a plurality of
inspectors and the plurality of properties, whereby:
i K p Q n ( i , p ) = Max ##EQU00004##
where k is the number of inspectors, and wherein Q is the number of
properties. In some cases, the inspector is further assigned to a
property based on their current availability and schedule.
Optionally, in some embodiments, two or more inspectors are
assigned to the same number of properties. Optionally, in some
embodiments, two or more inspectors are assigned to the different
number of properties. Optionally, in some embodiments, the number
of inspectors is 2 to about 10,000. Optionally, in some
embodiments, the number of inspectors is at least 2.
[0131] Once the inspectors are assigned to inspect a property based
on D and F 806, the inspection results are determined 807 by the
assigned inspector, and the database can be updated 808 with any
determined information. Optionally, in some embodiments, updating
the database 808 improves the machine learning capabilities in this
or other applications disclosed herein.
[0132] Per FIG. 9, in some embodiments, the application to
prioritize inspection of unpermitted renovation candidates and
validate the prioritization 900 is provided herein. Optionally, in
some embodiments, the application comprises a database 901. A list
of candidates with active renovation is provided 902. The
application determines the candidate-inspector distances (D) 903
and also estimates the remaining days until completion of active
renovation event (F) 904. The application then sorts the list of
candidates by ascending (F) values 905. Optionally, in some
embodiments, the application additionally or alternatively sorts
the list of candidates by an estimation of the value of the
renovation. The application continues by selecting an inspector to
inspect the candidate 906. Subsequently, the inspector can
determine inspection results 907, and update the database 908 with
any garnered information. Optionally, in some embodiments, the
application to assign unpermitted renovation visit to inspectors
900 is permitted to run automatically every period of time to
schedule and/or reschedule the property-inspector assignments.
Optionally, in some embodiments, the period of time is equal to,
one minute, thirty minutes, one hour, 12 hours, one day, one week,
one month, or one year.
[0133] Optionally, in some embodiments, the database comprises of
information from a plurality of data mining task processes.
Optionally, in some embodiments, the data mining task process
comprises a natural learning task process, numerical data mining
task process, or a photographic data mining task process.
Optionally, in some embodiments, the data mining task processes
incorporates feeds from sources that are photographic or
numerical.
[0134] Per FIG. 10, in some embodiments, the application to
prioritize inspection of unpermitted renovation events 1000 is
provided herein. Optionally, in some embodiments, the application
receives a list of candidates by ascending (F) values 1001. The
application then assigns one of each of the candidates with the
smallest (F) values to an inspector 1002. The application then
determines the remaining candidate-inspector distances (D) 1003.
The application then calculates n (F,P) for each remaining
candidate 1004. The application then assigns inspectors to inspect
a candidate to maximize n (F,P) 1005. Optionally, in some
embodiments, the application additionally or alternatively
prioritizes the list of candidates by an estimation of the value of
the renovation.
Detecting an Improper Real Estate Transfer Event
[0135] Additionally, provided herein are methods, systems, and
platforms, which employ various data sources and techniques to
identify undocumented current and past changes in ownership with
missing or fraudulent value re-assessments. Further, detection of
the true responsible directors and shareholders involved enables
swift and judicious prosecution of any guilty parties.
[0136] FIG. 11 shows an exemplary non-transitory computer-readable
storage media encoded with a computer program including
instructions executable by a processor to create an application to
detect an improper real estate transfer event 1100. Optionally, in
some embodiments, the application 1100 comprises a parameter
setting module 1101, a plurality of data ingestion interfaces 1102,
an improper transfer detection module 1104, an improper real estate
transfer probability calculation module 1105, and a validation
module 1106.
[0137] Optionally, in some embodiments, the parameter setting
module 1101 defines a data set to be evaluated. Optionally, in some
embodiments, the data set is defined by at least one of a street
address, a parcel, a street, a lot, a zip code, a county, a state,
an area drawn on a map, an area within a set radial distance from a
location, coordinates set by one or more satellites, an area within
a set driving distance of a location, a GPS point, and an area
defined by at least three GPS points.
[0138] Optionally, in some embodiments, each of the plurality of
data ingestion interfaces 1102 is connected to a unique external
interface 1103. Each interface can be configured to perform a data
mining task process to detect one or more real estate transfer
indicia within the data set. Optionally, in some embodiments, the
data mining task process comprises a natural language process,
numerical data mining process, or a photographic data mining task
process. Optionally, in some embodiments, the natural language task
process comprises syntax interpretation, semantic interpretation,
discourse interpretation, or speech interpretation. Optionally, in
some embodiments, the syntax interpretation comprises
lemmatization, morphological segmentation, part-of-speech tagging,
parsing, sentence boundary disambiguation, stemming, word
segmentation, or terminology extraction. Optionally, in some
embodiments, the semantic interpretation comprises lexical
semantics, machine translation, named entity recognition, natural
language generation, natural language understanding, optical
character recognition, question answering, recognizing textual
entailment, relationship extraction, sentiment analysis, topic
segmentation, or word sense disambiguation. Optionally, in some
embodiments, the discourse interpretation comprises automatic
summarization, coreference resolution, or discourse analysis.
Optionally, in some embodiments, the speech interpretation
comprises speech recognition, speech segmentation, and
text-to-speech. Optionally, in some embodiments, the external
interfaces 1103 comprises city property records, county property
records, city permit records, county permit records, post office
address database, state business records, historical real estate
listings, rental listings, demolition orders, dumpster orders,
portable restroom orders, customer account information from third
party companies, social media, phone records, address records,
historical credit card history purchase records, satellite images,
tax records, street views, online photographs, online videos, signs
outside a property, demolition orders, dumpster orders, portable
restroom orders, or the Internet.
[0139] Optionally, in some embodiments, the improper transfer
detection module 1104 applies a machine learning algorithm to
identify an initial candidate based on the real estate transfer
indicia within the data set. Optionally, in some embodiments, the
real estate indicia comprises a valuation of a property, a change
in a valuation of the property, a current ownership of the
property, a past ownership of the property, a lender on a property,
a ownership percentage of the property, or a liens on a property.
Optionally, in some embodiments, the machine learning algorithm
identifies an initial candidate if at least one of the current
ownership and the past ownership of the initial candidate comprises
a corporation. Optionally, in some embodiments, the machine
learning algorithm identifies an initial candidate if the
corporation comprises a title holding trust. Optionally, in some
embodiments, the machine learning algorithm identifies an initial
candidate if the ownership percentage of the property changes by
more than 49.9%. A title holding trust can comprise a trust by
which the real estate is conveyed to a trustee under an arrangement
reserving to the beneficiaries the full management and control of
the property. The beneficiaries of a title holding trust can be not
of public record. Optionally, in some embodiments, the machine
learning algorithm further determines the beneficiaries indirectly
through public records and media.
[0140] The improper real estate transfer probability calculation
module 1105 can calculate a probability that the improper real
estate transfer event has taken place at the initial candidate.
Optionally, in some embodiments, the calculation comprises applying
an increased weighted factor that the improper real estate transfer
event has taken place if at least one of the current ownership and
the past ownership of the initial candidate comprises a
corporation. Optionally, in some embodiments, the calculation
comprises applying an increased weighted factor that the improper
real estate transfer event has taken place if the corporation
comprises a title holding trust. Optionally, in some embodiments,
the calculation comprises applying an increased weighted factor
that the improper real estate transfer event has taken place if the
ownership percentage of the property changes by more than 49.9%.
Optionally, in some embodiments, the calculation comprises applying
an increased weighted factor that the improper real estate transfer
event has taken place if the ownership percentage of the property
changes without an associated assessment. Optionally, in some
embodiments, the calculation further applying an increased weighted
factor if the true beneficiaries have previously conducted an
improper transfer. Optionally, in some embodiments, the weight
factor comprises a parameter defining an importance associated with
a particular real estate transfer indicia or value of the real
estate transfer indicia.
[0141] Optionally, in some embodiments, the calculation comprises
calculating whether a probability threshold has been met.
Optionally, in some embodiments, the probability threshold can be
modified by the validation module based on the verified data. If
the corporation is detected to be a trust, the true ownership of
the initial candidate can be detected by identifying the beneficial
owners through public trust data, MLS data, social media data, or
any other public or semi-public source. The improper real estate
transfer event can comprise a misreported transaction value, a
misreported sales value, a misreported property value, or any
combination thereof.
[0142] Optionally, in some embodiments, the validation module 1106
accepts verified data 1107 regarding the real estate transfer event
and feeds back the verified data 1107 to the improper real estate
transfer probability calculation module 1105 to improve its
calculation over time. Optionally, in some embodiments, the
verified data 1107 is acquired by a public official inspecting the
candidate property. The verified data 1107 by one or more
inspectors can be received and/or distributed by any methods or
systems described herein.
[0143] Optionally, in some embodiments, the application further
comprises a historical transfer database 1108 receiving and storing
a plurality of the real estate transfer indicia from the plurality
of data ingestion interfaces. The historical transfer database 1108
can transmit one or more of the plurality of stored real estate
transfer indicia to the improper transfer detection module 1104.
Optionally, in some embodiments, the stored real estate transfer
indicia comprises a sequence of transfers regarding a real estate
unit. Optionally, in some embodiments, the historical transfer
database 1108 further receives a plurality of the initial
candidates from the improper real estate transfer detection module
1104 and appends the each of the initial candidates to at least one
of the stored real estate transfer indicia. Optionally, in some
embodiments, the historical transfer database 1108 stores verified
data 1107. Optionally, in some embodiments, the improper transfer
detection module 1104 applies the machine learning algorithm to
identify the initial candidate based further on the initial
candidates appended to the plurality of stored real estate transfer
indicia.
[0144] The stored real estate transfer indicia can comprise real
estate indicia over a certain period of time. The stored real
estate transfer indicia can comprise a consecutive series of real
estate indicia over a certain period of time. Storing the real
estate transfer indicia can comprise appending the real estate
transfer indicia to records associated with the property, the
buyer, the seller, the loan officer, the zip code, or any
combination thereof. In some embodiments, the historical transfer
database remembers a sequence of transfers.
Determining when One or More Unpermitted Renovation Events has
Taken Place
[0145] Additionally, provided herein are methods, systems, and
platforms, which employ various data sources and techniques to
determine when one or more unpermitted renovation events has taken
place. Further, detection of the time of the unpermitted renovation
enables accurate and fair collection of associated renovation
taxes.
[0146] It is assumed a particular property as having been renovated
at some time in the past has been identified, and hence that the
current assessment is incorrect and probably undervalued. The
appropriate authority would like to appropriately re-assess the
property to increase the amount of property tax collected in
future.
[0147] Federal, state, and county real estate taxes can employ
"escape fees" to collect back taxes for misassessed valuations. For
example, if the square footage of the property was recorded in
error by the government, the property owner can owe four years of
escape fees. However if the misassessment is at the fault of the
owners, escape fees can be charged, for instance, for up to eight
years. The escape fee can be dependent on the term during which the
real estate property was incorrectly valued. As such, knowledge of
the start date of such renovations is greatly advantageous towards
proper escape fee collection.
[0148] FIG. 12 shows an exemplary non-transitory computer-readable
storage media encoded with a computer program including
instructions executable by a processor to create an application to
determine when one or more unpermitted renovation events has taken
place to an unpermitted renovation candidate. Optionally, in some
embodiments, the application 1200 comprises: an unpermitted
renovation candidate module 1201, a parameter setting module 1202,
a set of first data ingestion interfaces 1203, a set of second data
ingestion interfaces 1204, a renovation timing estimation module
1205, and a validation module 1206. In some embodiments, the
application 1200 further comprises a set of third data ingestion
interfaces, a fourth set of data ingestion interfaces, or more sets
of data ingestion interfaces. In some embodiments, at least one of
the set of third data ingestion interfaces, the fourth set of data
ingestion interfaces, or more of the sets of data ingestion
interfaces can be initiated by a user.
[0149] In some embodiments, the application 1200 further comprises
a second data source filter module. The second data source filter
module can be configured to allow a user to filter the second data
mining task process to the second data source.
[0150] The unpermitted renovation candidate module 1201 can present
an unpermitted renovation candidate. The unpermitted renovation
candidate can comprise an address, a GPS coordinate, a land plot
indicator, or any combination thereof. The parameter setting module
1202 can define a data set to be evaluated.
[0151] Each of the first data ingestion interfaces 1203 can connect
to a first data source. Each of the first data ingestion interfaces
1203 can be configured to perform a data mining task process to a
first data source. The data mining task process can determine an
initial time range within the data set. The initial time range can
represent when at least one unpermitted renovation event has taken
place at the unpermitted renovation candidate. In some embodiments,
the initial time range comprises a time range from a current time
to when the unpermitted renovation event was assessed according to
the first data source. In some embodiments, the first data source
comprises city property records, county property records, city
permit records, county permit records, and state business
records.
[0152] Each of the second set of second data ingestion interfaces
1204 can connect to a second data source. Each second data
ingestion interfaces 1204 can be configured to perform a data
mining task process to the second data source. The data mining task
process can detect one or more unpermitted renovation timing
indicia within the data set when the at least one unpermitted
renovation event has taken place at the unpermitted renovation
candidate. In some embodiments, the second data source comprises
public sources, licensed data feeds, sources depicting historical
water usage at the unpermitted renovation candidate, sources
depicting historical energy usage at the unpermitted renovation
candidate, contractor web sites, Yelp, Craigslist, Wayback Machine,
financial documents, photographs from aerial surveys, Google Earth,
Google Streetview, rental records for dumpsters, rental records for
portable restrooms, serial numbers, manufacturer warranty records,
Home Owner's Association records, historical real estate listings,
rental listings, demolition orders, dumpster orders, portable
restroom orders, customer account information from third party
companies, social media, phone records, address records, historical
credit card history purchase records, satellite images, tax
records, street views, online photographs, online videos, signs
outside a property, demolition orders, dumpster orders, portable
restroom orders, or the Internet.
[0153] In some embodiments, a third data ingestion interfaces can
be configured to perform a data mining task process to a third data
source. In some embodiments, a fourth data ingestion interfaces can
be configured to perform a data mining task process to a fourth
data source. The third data source can comprise a data source from
the first data source, the second data source, or both. The fourth
data source can comprise a data source from the first data source,
the second data source, the third data source, or both. The third
data source can comprise a data source that is not in the first
data source, the second data source, or both. The fourth data
source can comprise a data source that is not in the first data
source, the second data source, or both.
[0154] In some embodiments, at least one of the first data source
and the second data source comprises contractor records of
renovations, contractor website photos, or contractor website
testimonials. In some embodiments, at least one of the first data
source and the second data source comprises an online review or an
online listing by a contractor. In some embodiments, at least one
of the first data source and the second data source comprises
publicly available website data that is no longer actively
displayed. Such archival data can be associated with a time of
publication. Such archival data can be received by such sources as
"the Wayback Machine." In some embodiments, at least one of the
first data source and the second data source comprises a
manufacturer warranty record including a date of installation.
[0155] The renovation timing estimation module 1205 can apply a
machine learning algorithm to present a refined renovation time
range. The renovation timing estimation module 1205 can
alternatively or further apply a rule-based algorithm to present
the refined renovation time range. In some embodiments, the
renovation timing estimation module 1205 feeds input to the first
ingestion interface 1203 to allow the first ingestion interface to
focus its ingestion. In some embodiments, the renovation timing
estimation module 1205 feeds input to the second ingestion
interface 1204 to allow the second ingestion interface to focus its
ingestion. The renovation timing estimation module 1205 can apply a
machine learning algorithm to present a refined renovation time
range based on the detected initial time range and the detected
unpermitted renovation timing indicia. In some embodiments, the
refined renovation time range comprises a narrower time range than
the initial time range. In some embodiments, the unpermitted
renovation timing indicia comprises increase in water usage,
decrease in water usage, increase in energy usage, decrease in
energy usage, permanent change in water usage, permanent change in
energy usage, records of renovations from Internet sources,
documentation reflecting refinanced mortgages, documentation
reflecting home equity lines of credit, photographs depicting
structural changes, records reflecting renovation work, records
reflecting renovation waste, serial numbers reflecting new
appliances, windows, or air conditioners, or manufacturer warranty
records reflecting dates of installation. The renovation timing
estimation module 1205 can further apply a machine learning
algorithm to present a further refined renovation time range based
on the detected unpermitted renovation indicia generated by the set
of third data ingestion interfaces, the fourth set of data
ingestion interfaces, or by more sets of data ingestion interfaces.
The renovation timing estimation module 1205 can further apply a
machine learning algorithm to present a first refined renovation
time range, a second renovation time range, or more renovation time
ranges based on the detected unpermitted renovation indicia
generated by the set of third data ingestion interfaces, the fourth
set of data ingestion interfaces, or by more sets of data ingestion
interfaces.
[0156] An increase or decrease in water usage can indicate an
unpermitted renovation event comprising the addition of landscaping
features, a swimming pool, a kitchen, a bathroom, a sink, or any
combination thereof. An increase or decrease in electricity usage
can indicate an unpermitted renovation event comprising the
addition of rooms, heating and ventilation equipment, kitchens, or
both. A sudden increase in energy use can indicate the use of
construction tools during an unpermitted renovation event.
[0157] At least one of the second data and the unpermitted
renovation timing indicia can comprise aerial surveys, Google
Earth, Google Streetview and other images, wherein at least one of
the data mining task process and the machine learning algorithm
performs a historical comparison of images, 3D data, or both to
detect structure changes over time, evidence of construction
workers and demolition, presence of dumpsters, bare roofs. At least
one of the second data and the unpermitted renovation timing
indicia can comprise financial documents such as refinanced
mortgages and home equity lines of credit, which can be indicative,
via the data mining task process or the machine learning algorithm,
of the date of a renovation and the renovation value. At least one
of the second data and the unpermitted renovation timing indicia
can comprise a manufacturer instillation warrantee, wherein at
least one of the data mining task process and the machine learning
algorithm associate the date of installation therein can be
associated with a candidate real estate property. At least one of
the second data and the unpermitted renovation timing indicia can
comprise HOA records wherein at least one of the data mining task
process and the machine learning algorithm are configured to detect
a requested renovation date. Some of the second data and the
unpermitted renovation timing indicia can indicate that renovation
that work was in progress on particular dates. Combinations of the
second data and the unpermitted renovation timing indicia can data
items might be used to determine if there were more than one
renovation projects for the same candidate property.
[0158] The validation module 1206 can accept verified data
regarding the timing of the unpermitted renovation event. The
validation module 1206 can further feed back the verified data to
the renovation timing estimation module 1205 to improve its
prediction over time.
Detecting an Improper Residency Status for a Real Estate
Property
[0159] Additionally, provided herein are methods, systems, and
platforms, which employ various data sources and techniques to
detect an improper residency status for a real estate property.
Further, detection of improper residency status for a real estate
property enables accurate and fair collection of associated
residency taxes. The residency status can comprise a primary
residence status and a vacation residence status. Primary residence
status can be defined at a real estate property at which the owner
or owners resides for more than half of the year. Vacation
residence status can be defined at a real estate property at which
the owner or owners resides for less than half of the year.
Ownership of a primary residence is often associated with different
tax laws and requirements than ownership of a vacation residence.
Mortgage interest are only be deducted on properties that are used
exclusively as a residence. Such improper residency status can
comprise claiming a real estate property as a primary residence
when it is a vacation residence status. Further, residency status
can be used to determine which school or school district a child
can attend.
[0160] FIG. 13 shows an exemplary non-transitory computer-readable
storage media encoded with a computer program including
instructions executable by a processor to create an application to
detect an improper residency status for a real estate property.
Optionally, in some embodiments, the application 1300 comprises a
parameter setting module 1301, a plurality of data ingestion
interfaces 1302, an improper residency detection module 1303, a
residency probability calculation module 1304, and a validation
module 1305.
[0161] The parameter setting module 1301 can define a data set to
be evaluated. In some embodiments, the data set is defined by at
least one of a street address, a parcel, a street, a lot, a zip
code, a county, a state, an area drawn on a map, an area within a
set radial distance from a location, coordinates set by one or more
satellites, an area within a set driving distance of a location, a
GPS point, and an area defined by at least three GPS points.
[0162] Each data ingestion interface 1302 can connect to a unique
external data source. Each data ingestion interface 1302 can be
configured to perform a data mining task process to its data
source. The data mining task process can detect one or more
improper residency indicia within the data set.
[0163] In some embodiments, the data mining task process comprises
a natural language process, numerical data mining process, a
photographic data mining task process, or any combination thereof.
In some embodiments, the natural language task process comprises
syntax interpretation, semantic interpretation, discourse
interpretation, speech interpretation, or any combination thereof.
In some embodiments, the syntax interpretation comprises
lemmatization, morphological segmentation, part-of-speech tagging,
parsing, sentence boundary disambiguation, stemming, word
segmentation, terminology extraction, or any combination thereof.
In some embodiments, the semantic interpretation comprises lexical
semantics, machine translation, named entity recognition, natural
language generation, natural language understanding, optical
character recognition, question answering, recognizing textual
entailment, relationship extraction, sentiment analysis, topic
segmentation, word sense disambiguation, or any combination
thereof. In some embodiments, the discourse interpretation
comprises automatic summarization, coreference resolution,
discourse analysis, or any combination thereof. In some
embodiments, the speech interpretation comprises speech
recognition, speech segmentation, text-to-speech, or both.
[0164] In some embodiments, the external data source comprises city
property records, county property records, city permit records,
county permit records, post office address database, state business
records, historical real estate listings, rental listings,
demolition orders, dumpster orders, portable restroom orders,
customer account information from third party companies, social
media, phone records, phone location records, cellphone location,
address records, historical credit card history purchase records,
satellite images, tax records, street views, online photographs,
online videos, signs outside a property, demolition orders,
dumpster orders, portable restroom orders, the Internet, or any
combination thereof.
[0165] In some embodiments, the detection of one or more improper
residency indicia comprises water usage change, electricity usage
change, gas usage change, street parking occupancy change, driveway
parking occupancy change, package delivery frequency change, window
adjustment frequency change, visible room light frequency change, a
street-side trash can placement frequency change, a mailbox flag
status frequency change, a garage door status frequency change a
frequency of phone calls, a frequency of credit card purchases, or
any combination thereof. Each improper residency indicia can be
associated with a weight based on the correlation between that
indicia and the probability of the improper residency status.
[0166] An increase in at least one of the water, electricity and
gas usage can provide an improper residency indicia that a property
listed as a vacation residence can actually comprise a primary
residence. A decrease in water usage can provide an improper
residency indicia that a property listed as a primary residence can
actually comprise a vacation residence. Increased surrounding
street parking occupancy, driveway parking occupancy, or both at a
real estate property can provide an improper residency indicia that
a property listed as a vacation residence can actually comprise a
primary residence. Decreased surrounding street parking occupancy,
driveway parking occupancy, or both at a real estate property can
provide an improper residency indicia that a property listed as a
primary residence can actually comprise a vacation residence. An
increase in the frequency of package deliveries, window adjustment,
visible room light changes, street-side trash can placement,
mailbox flag status, garage door opening and closing, phone calls,
credit card purchases or any combination thereof can provide an
improper residency indicia that a property listed as a vacation
residence can actually comprise a primary residence. A decrease in
the frequency of package deliveries, window adjustment, visible
room light changes, street-side trash can placement, mailbox flag
status frequency, garage door opening and closing, phone calls,
cellphone location, credit card purchases, or any combination
thereof can provide an improper residency indicia that a property
listed as a primary residence can actually comprise a vacation
residence.
[0167] The package delivery frequency can comprise a number of
packages delivered to the address within a set period. The window
adjustment can comprise a frequency at which a window is opened, a
window is closed, a window shade is opened, a window shade is
closed, or any combination thereof. The visible room light
frequency can comprise a frequency at which an interior or exterior
light is turned on and off. The street-side trash can placement
frequency can comprise a frequency at which trash is deposited on
the street for pickup. The mailbox flag status frequency can
comprise a frequency at which the mailbox flag which signals
outgoing mail is raised. The garage door opening and closing
frequency can comprise a frequency at which the garage door is
opened or closed. The phone calls can be associated with the
candidate property. The frequency of credit card purchases can be
associated with an account that lists the candidate property
[0168] The improper residency detection module 1303 can apply a
machine learning algorithm to identify an initial candidate. The
improper residency detection module 1303 can apply a machine
learning algorithm to identify an initial candidate based on the
improper residency indicia within the data set. The improper
residency detection module 1303 can alternatively or additionally
apply a rule-based algorithm to identify an initial candidate.
[0169] The residency probability calculation module 1304 can
calculate a probability that the initial candidate has an improper
residency status.
[0170] The validation module 1305 can accept verified data
regarding the residency status. The validation module 1305 can
further feed back the verified data to the improper residency
probability calculation module 1304. The feed back the verified
data to the improper residency probability calculation module 1304
can improve the calculations of the improper residency probability
calculation module 1304 over time.
Detect an Improper Occupancy Tax Status for a Real Estate
Property
[0171] Additionally, provided herein are methods, systems, and
platforms, which employ various data sources and techniques to
detect an improper occupancy tax status for a real estate property.
Further, detection of improper occupancy tax status for a real
estate property enables accurate and fair collection of associated
occupancy taxes. In some embodiments, the methods, systems, and
platforms can detect improper occupancy tax status for a plurality
of properties.
[0172] FIG. 14 shows an exemplary non-transitory computer-readable
storage media encoded with a computer program including
instructions executable by a processor to create an application to
detect an improper residency status for a real estate property.
Optionally, in some embodiments, the application 1400 comprises a
parameter setting module 1401, a plurality of data ingestion
interfaces 1402, an improper occupancy tax detection module 1403,
an occupancy tax probability calculation module 1404, and a
validation module 1405.
[0173] The parameter setting module 1401 can define a data set to
be evaluated. In some embodiments, the data set is defined by at
least one of a street address, a parcel, a street, a lot, a zip
code, a county, a state, an area drawn on a map, an area within a
set radial distance from a location, coordinates set by one or more
satellites, an area within a set driving distance of a location, a
GPS point, and an area defined by at least three GPS points.
[0174] Each of the plurality of data ingestion interfaces 1402 can
connect to a unique external data source. Each interface can be
configured to perform a data mining task process to its data
source. Each interface can be configured to perform a data mining
task process to its data source to detect one or more improper
occupancy tax indicia within the data set.
[0175] The improper occupancy tax detection module 1403 can apply a
machine learning algorithm to identify an initial candidate. The
improper occupancy tax detection module 1403 can apply a machine
learning algorithm to identify an initial candidate based on the
improper occupancy tax indicia within the data set. The improper
occupancy tax detection module 1403 can further or alternatively
apply a rule-based algorithm to identify an initial candidate. In
some embodiments, the data mining task process comprises a natural
language process, numerical data mining process, a photographic
data mining task process, or any combination thereof. In some
embodiments, the natural language task process comprises syntax
interpretation, semantic interpretation, discourse interpretation,
speech interpretation, or any combination thereof. In some
embodiments, the syntax interpretation comprises lemmatization,
morphological segmentation, part-of-speech tagging, parsing,
sentence boundary disambiguation, stemming, word segmentation,
terminology extraction, or any combination thereof. In some
embodiments, the semantic interpretation comprises lexical
semantics, machine translation, named entity recognition, natural
language generation, natural language understanding, optical
character recognition, question answering, recognizing textual
entailment, relationship extraction, sentiment analysis, topic
segmentation, word sense disambiguation, or any combination
thereof. In some embodiments, the discourse interpretation
comprises automatic summarization, coreference resolution,
discourse analysis, or any combination thereof. In some
embodiments, the speech interpretation comprises speech
recognition, speech segmentation, and text-to-speech, or any
combination thereof. In some embodiments, the external data source
comprises AirBnB, VRBO, city property records, county property
records, city permit records, county permit records, post office
address database, state business records, historical real estate
listings, rental listings, demolition orders, dumpster orders,
portable restroom orders, customer account information from third
party companies, social media, phone records, address records,
historical credit card history purchase records, satellite images,
tax records, street views, online photographs, online videos, signs
outside a property, demolition orders, dumpster orders, portable
restroom orders, the Internet, or any combination thereof.
[0176] In some embodiments, the detection of one or more improper
occupancy tax indicia comprises water usage change, electricity
usage change, gas usage change, street parking occupancy change,
driveway parking occupancy change, package delivery frequency
change, window adjustment frequency change, visible room light
frequency change, a street-side trash can placement frequency
change, a mailbox flag status frequency change, a garage door
status frequency change, or any combination thereof. Each improper
occupancy tax indicia can be associated with a weight based on the
correlation between that indicia and the probability of the
improper occupancy tax status.
[0177] An increase in at least one of the water, electricity, and
gas usage can provide an improper occupancy tax indicia that a
residency number can be underreported. A decrease in water usage
can provide an improper occupancy tax indicia that a residency
number can be overreported. Increased surrounding street parking
occupancy, driveway parking occupancy, or both at a real estate
property can provide an improper occupancy tax indicia that a
residency number can be underreported. Decreased surrounding street
parking occupancy, driveway parking occupancy, or both at a real
estate property can provide an improper occupancy tax indicia that
a residency number can be overreported. An increase in the
frequency of package deliveries, window adjustment, visible room
light changes, street-side trash can placement, mailbox flag
status, garage door opening and closing, phone calls, credit card
purchases or any combination thereof can provide an improper
occupancy tax indicia that a residency number can be underreported.
A decrease in the frequency of package deliveries, window
adjustment, visible room light changes, street-side trash can
placement, mailbox flag status frequency, garage door opening and
closing, phone calls, credit card purchases, or any combination
thereof can provide an improper occupancy tax indicia that a
residency number can be overreported.
[0178] The package delivery frequency can comprise a number of
packages delivered to the address within a set period. The window
adjustment can comprise a frequency at which a window is opened, a
window is closed, a window shade is opened, a window shade is
closed, or any combination thereof. The visible room light
frequency can comprise a frequency at which an interior or exterior
light is turned on and off. The street-side trash can placement
frequency can comprise a frequency at which trash is deposited on
the street for pickup. The mailbox flag status frequency can
comprise a frequency at which the mailbox flag which signals
outgoing mail is raised. The garage door opening and closing
frequency can comprise a frequency at which the garage door is
opened or closed. The phone calls can be associated with the
candidate property. The frequency of credit card purchases can be
associated with an account that lists the candidate property
[0179] The occupancy tax probability calculation module 1404 can
calculate a probability that the initial candidate has an improper
occupancy tax status;
[0180] The validation module 1405 can accept verified data
regarding the occupancy tax status. The validation module 1405 can
further feed back the verified data to the improper occupancy tax
probability calculation module 1404 to improve its calculation over
time.
Machine Learning
[0181] In some embodiments, machine learning algorithms are
utilized to aid in determining a consumer's preferred design
elements. In some embodiments, the machine learning algorithm is
used to detect an unpermitted renovation event, validate the
detected event, or both.
[0182] In some embodiments, machine learning algorithms are
utilized by the data ingestion interfaces to perform the data
mining task, to detect one or more unpermitted renovation event
indicia, or both. In some embodiments, machine learning algorithms
are utilized by the renovation detection module to identify an
initial candidate based on the detection indicia. In some
embodiments, the machine learning algorithms utilized by the
renovation detection module employ one or more forms of labels
including but not limited to human annotated labels and
semi-supervised labels. The human annotated labels can be provided
by a hand-crafted heuristic. For example, the hand-crafted
heuristic can comprise examining differences between public and
county records. The semi-supervised labels can be determined using
a clustering technique to find properties similar to those flagged
by previous human annotated labels and previous semi-supervised
labels. The semi-supervised labels can employ a XGBoost, a neural
network, or both.
[0183] In some embodiments, machine learning algorithms are
utilized by the renovation probability calculation module to
calculate a probability that an unpermitted renovation event has
taken or is taking place at the initial candidate. In some
embodiments, the renovation probability calculation module
calculates the probability that the unpermitted renovation event
has taken or is taking place at the initial candidate using a
distant supervision method. The distant supervision method can
create a large training set seeded by a small hand-annotated
training set. The distant supervision method can comprise
positive-unlabeled learning with the training set as the `positive`
class. The distant supervision method can employ a logistic
regression model, a recurrent neural network, or both. The
recurrent neural network can be advantageous for Natural Language
Processing (NLP) machine learning.
[0184] Examples of machine learning algorithms can include a
support vector machine (SVM), a naive Bayes classification, a
random forest, a neural network, deep learning, or other supervised
learning algorithm or unsupervised learning algorithm for
classification and regression. The machine learning algorithms can
be trained using one or more training datasets.
[0185] In some embodiments, the machine learning algorithm utilizes
regression modeling, wherein relationships between predictor
variables and dependent variables are determined and weighted. In
one embodiment, for example, initial candidate can be a dependent
variable and is derived from the detection indicia within the data
set. In another embodiment, the one or more unpermitted renovation
event indicia is a dependent variable derived from unique external
data source. In yet another embodiment, the probability that an
unpermitted renovation event has taken or is taking place at the
initial candidate is a dependent variable derived from the
following predictor variables: one or more unpermitted renovation
event indicia, the unique external data source, and the data
set.
[0186] In some embodiments, a machine learning algorithm is used to
select catalogue images and recommend project scope. A non-limiting
example of a multi-variate linear regression model algorithm is
seen below:
probability=A.sub.0+A.sub.1(X.sub.1)+A.sub.2(X.sub.2)+A.sub.3(X.sub.3)+A.-
sub.4(X.sub.4)+A.sub.5(X.sub.5)+A.sub.6(X.sub.6)+A.sub.7(X.sub.7) .
. . wherein A.sub.1 (A.sub.1, A.sub.2, A.sub.3, A.sub.4, A.sub.5,
A.sub.6, A.sub.7, . . . ) are "weights" or coefficients found
during the regression modeling; and X.sub.1 (X.sub.1, X.sub.2,
X.sub.3, X.sub.4, X.sub.5, X.sub.6, X.sub.7, . . . ) are data
collected from the User. Any number of A.sub.1 and X.sub.1 variable
can be included in the model. For example, in a non-limiting
example wherein there are 7 X.sub.1 terms, X.sub.1 is the number of
unpermitted renovation event indicia, X.sub.2 is the number of
initial candidates, and X.sub.3 is the probability that an
unpermitted renovation event has taken or is taking place at the
initial candidate. In some embodiments, the programming language
"R" is used to run the model.
[0187] In some embodiments, training comprises multiple steps. In a
first step, an initial model is constructed by assigning
probability weights to predictor variables. In a second step, the
initial model is used to "recommend" initial candidates. In a third
step, the validation module accepts verified data regarding the
unpermitted renovation event and feeds back the verified data to
the renovation probability calculation. At least one of the first
step, the second step, and the third step can repeat one or more
times continuously or at set intervals.
Digital Processing Device
[0188] Optionally, in some embodiments, the platforms, systems,
media, and methods described herein include a digital processing
device, or use of the same. In further embodiments, the digital
processing device includes one or more hardware central processing
units (CPUs) or general purpose graphics processing units (GPGPUs)
that carry oust the device's functions. In still further
embodiments, the digital processing device further comprises an
operating system configured to perform executable instructions.
Optionally, in some embodiments, the digital processing device is
optionally connected a computer network. In further embodiments,
the digital processing device is optionally connected to the
Internet such that it accesses the World Wide Web. In still further
embodiments, the digital processing device is optionally connected
to a cloud computing infrastructure. In other embodiments, the
digital processing device is optionally connected to an intranet.
In other embodiments, the digital processing device is optionally
connected to a data storage device.
[0189] In accordance with the description herein, suitable digital
processing devices include, by way of non-limiting examples, server
computers, desktop computers, laptop computers, notebook computers,
sub-notebook computers, netbook computers, netpad computers,
set-top computers, and media streaming devices, handheld computers,
Internet appliances, mobile smartphones, tablet computers, personal
digital assistants, video game consoles, and vehicles. Those of
skill in the art will recognize that many smartphones are suitable
for use in the system described herein. Those of skill in the art
will also recognize that select televisions, video players, and
digital music players with optional computer network connectivity
are suitable for use in the system described herein. Suitable
tablet computers include those with booklet, slate, and convertible
configurations, known to those of skill in the art.
[0190] Optionally, in some embodiments, the digital processing
device includes an operating system configured to perform
executable instructions. The operating system is, for example,
software, including programs and data, which manages the device's
hardware and provides services for execution of applications. Those
of skill in the art will recognize that suitable server operating
systems include, by way of non-limiting examples, FreeBSD, OpenBSD,
NetBSD.RTM., Linux, Apple.RTM. Mac OS X Server.RTM., Oracle.RTM.
Solaris.RTM., Windows Server.RTM., and Novell.RTM. NetWare.RTM..
Those of skill in the art will recognize that suitable personal
computer operating systems include, by way of non-limiting
examples, Microsoft Windows.RTM., Apple Mac OS X.RTM., UNIX.RTM.,
and UNIX-like operating systems such as GNU/Linux.RTM.. Optionally,
in some embodiments, the operating system is provided by cloud
computing. Those of skill in the art will also recognize that
suitable mobile smart phone operating systems include, by way of
non-limiting examples, Nokia.RTM. Symbian.RTM. OS, Apple.RTM.
iOS.RTM., Research In Motion.RTM. BlackBerry OS.RTM., Google.RTM.
Android.RTM., Microsoft.RTM. Windows Phone.RTM. OS, Microsoft.RTM.
Windows Mobile.RTM. OS, Linux.RTM., and Palm.RTM. WebOS.RTM.. Those
of skill in the art will also recognize that suitable media
streaming device operating systems include, by way of non-limiting
examples, Apple TV.RTM., Roku.RTM., Boxee.RTM., Google TV.RTM.,
Google Chromecast.RTM., Amazon Fire.RTM., and Samsung.RTM.
HomeSync.RTM.. Those of skill in the art will also recognize that
suitable video game console operating systems include, by way of
non-limiting examples, Sony.RTM. PS3.RTM., Sony PS4.RTM.,
Microsoft.RTM. Xbox 360.RTM., Microsoft Xbox One, Nintendo.RTM.
Wii.RTM., Nintendo.RTM. Wii U.RTM., and Ouya.RTM..
[0191] Optionally, in some embodiments, the device includes a
storage and/or memory device. The storage and/or memory device is
one or more physical apparatuses used to store data or programs on
a temporary or permanent basis. Optionally, in some embodiments,
the device is volatile memory and requires power to maintain stored
information. Optionally, in some embodiments, the device is
non-volatile memory and retains stored information when the digital
processing device is not powered. In further embodiments, the
non-volatile memory comprises flash memory. Optionally, in some
embodiments, the non-volatile memory comprises dynamic
random-access memory (DRAM). Optionally, in some embodiments, the
non-volatile memory comprises ferroelectric random access memory
(FRAM). Optionally, in some embodiments, the non-volatile memory
comprises phase-change random access memory (PRAM). In other
embodiments, the device is a storage device including, by way of
non-limiting examples, CD-ROMs, DVDs, flash memory devices,
magnetic disk drives, magnetic tapes drives, optical disk drives,
and cloud computing based storage. In further embodiments, the
storage and/or memory device is a combination of devices such as
those disclosed herein.
[0192] Optionally, in some embodiments, the digital processing
device includes a display to send visual information to a user.
Optionally, in some embodiments, the display is a liquid crystal
display (LCD). In further embodiments, the display is a thin film
transistor liquid crystal display (TFT-LCD). Optionally, in some
embodiments, the display is an organic light emitting diode (OLED)
display. In various further embodiments, on OLED display is a
passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED)
display. Optionally, in some embodiments, the display is a plasma
display. In other embodiments, the display is a video projector. In
yet other embodiments, the display is a head-mounted display in
communication with the digital processing device, such as a VR
headset. In further embodiments, suitable VR headsets include, by
way of non-limiting examples, HTC Vive, Oculus Rift, Samsung Gear
VR, Microsoft HoloLens, Razer OSVR, FOVE VR, Zeiss VR One, Avegant
Glyph, Freefly VR headset, and the like. In still further
embodiments, the display is a combination of devices such as those
disclosed herein.
[0193] Optionally, in some embodiments, the digital processing
device includes an input device to receive information from a user.
Optionally, in some embodiments, the input device is a keyboard.
Optionally, in some embodiments, the input device is a pointing
device including, by way of non-limiting examples, a mouse,
trackball, track pad, joystick, game controller, or stylus.
Optionally, in some embodiments, the input device is a touch screen
or a multi-touch screen. In other embodiments, the input device is
a microphone to capture voice or other sound input. In other
embodiments, the input device is a video camera or other sensor to
capture motion or visual input. In further embodiments, the input
device is a Kinect, Leap Motion, or the like. In still further
embodiments, the input device is a combination of devices such as
those disclosed herein.
[0194] FIG. 4 shows a schematic diagram of a digital processing
device; in this case, a device with one or more CPUs, a memory, a
communication interface, and a display, in accordance with some
embodiments. Referring to FIG. 4, in a particular embodiment, a
digital processing device 401 is programmed or otherwise configured
to create an application to detect an unpermitted renovation event
and validate the detected event. The non-transitory
computer-readable storage media 401 is programmed or otherwise
configured to create an application to detect an unpermitted
renovation event and validate the detected event. In this
embodiment, the digital processing device 401 includes a central
processing unit (CPU, also "processor" and "computer processor"
herein) 405, which is optionally a single core, a multi core
processor, or a plurality of processors for parallel processing.
The digital processing device 401 also includes memory or memory
location 410 (e.g., random-access memory, read-only memory, flash
memory), electronic storage unit 415 (e.g., hard disk),
communication interface 420 (e.g., network adapter) for
communicating with one or more other systems, and peripheral
devices 425, such as cache, other memory, data storage and/or
electronic display adapters. The memory 410, storage unit 415,
interface 420 and peripheral devices 425 are in communication with
the CPU 405 through a communication bus (solid lines), such as a
motherboard. The storage unit 415 comprises a data storage unit (or
data repository) for storing data. The digital processing device
401 is optionally operatively coupled to a computer network
("network") 430 with the aid of the communication interface 420.
The network 430, in various cases, is the internet, an internet,
and/or extranet, or an intranet and/or extranet that is in
communication with the internet. The network 430, in some cases, is
a telecommunication and/or data network. The network 430 optionally
includes one or more computer servers, which enable distributed
computing, such as cloud computing. The network 430, in some cases,
with the aid of the device 401, implements a peer-to-peer network,
which enables devices coupled to the device 401 to behave as a
client or a server.
[0195] Continuing to refer to FIG. 4, the CPU 405 is configured to
execute a sequence of machine-readable instructions, embodied in a
program, application, and/or software. The instructions are
optionally stored in a memory location, such as the memory 410. The
instructions are directed to the CPU 105, which subsequently
program or otherwise configure the CPU 405 to implement methods of
the present disclosure. Examples of operations performed by the CPU
405 include fetch, decode, execute, and write back. The CPU 405 is,
in some cases, part of a circuit, such as an integrated circuit.
One or more other components of the device 401 are optionally
included in the circuit. In some cases, the circuit is an
application specific integrated circuit (ASIC) or a field
programmable gate array (FPGA).
[0196] Continuing to refer to FIG. 4, the storage unit 415
optionally stores files, such as drivers, libraries and saved
programs. The storage unit 415 optionally stores user data, e.g.,
user preferences and user programs. The digital processing device
401, in some cases, includes one or more additional data storage
units that are external, such as located on a remote server that is
in communication through an intranet or the internet.
[0197] Continuing to refer to FIG. 4, the digital processing device
401 optionally communicates with one or more remote computer
systems through the network 430. For instance, the device 401
optionally communicates with a remote computer system of a user.
Examples of remote computer systems include personal computers
(e.g., portable PC), slate or tablet PCs (e.g., Apple iPad,
Samsung.RTM. Galaxy Tab, etc.), smartphones (e.g., Apple.RTM.
iPhone, Android-enabled device, Blackberry.RTM., etc.), or personal
digital assistants.
[0198] Methods as described herein are optionally implemented by
way of machine (e.g., computer processor) executable code stored on
an electronic storage location of the digital processing device
401, such as, for example, on the memory 410 or electronic storage
unit 415. The machine executable or machine readable code is
optionally provided in the form of software. During use, the code
is executed by the processor 405. In some cases, the code is
retrieved from the storage unit 415 and stored on the memory 410
for ready access by the processor 405. In some situations, the
electronic storage unit 415 is precluded, and machine-executable
instructions are stored on the memory 410.
Non-Transitory Computer Readable Storage Medium
[0199] Optionally, in some embodiments, the platforms, systems,
media, and methods disclosed herein include one or more
non-transitory computer readable storage media encoded with a
program including instructions executable by the operating system
of an optionally networked digital processing device. In further
embodiments, a computer readable storage medium is a tangible
component of a digital processing device. In still further
embodiments, a computer readable storage medium is optionally
removable from a digital processing device. Optionally, in some
embodiments, a computer readable storage medium includes, by way of
non-limiting examples, CD-ROMs, DVDs, flash memory devices, solid
state memory, magnetic disk drives, magnetic tape drives, optical
disk drives, cloud computing systems and services, and the like. In
some cases, the program and instructions are permanently,
substantially permanently, semi-permanently, or non-transitorily
encoded on the media.
Computer Program
[0200] Optionally, in some embodiments, the platforms, systems,
media, and methods disclosed herein include at least one computer
program, or use of the same. A computer program includes a sequence
of instructions, executable in the digital processing device's CPU,
written to perform a specified task. Computer readable instructions
can be implemented as program modules, such as functions, objects,
Application Programming Interfaces (APIs), data structures, and the
like, that perform particular tasks or implement particular
abstract data types. In light of the disclosure provided herein,
those of skill in the art will recognize that a computer program
can be written in various versions of various languages.
[0201] The functionality of the computer readable instructions can
be combined or distributed as desired in various environments.
Optionally, in some embodiments, a computer program comprises one
sequence of instructions. Optionally, in some embodiments, a
computer program comprises a plurality of sequences of
instructions. Optionally, in some embodiments, a computer program
is provided from one location. In other embodiments, a computer
program is provided from a plurality of locations. In various
embodiments, a computer program includes one or more software
modules. In various embodiments, a computer program includes, in
part or in whole, one or more web applications, one or more mobile
applications, one or more standalone applications, one or more web
browser plug-ins, extensions, add-ins, or add-ons, or combinations
thereof.
Web Application
[0202] Optionally, in some embodiments, a computer program includes
a web application. In light of the disclosure provided herein,
those of skill in the art will recognize that a web application, in
various embodiments, utilizes one or more software frameworks and
one or more database systems. Optionally, in some embodiments, a
web application is created upon a software framework such as
Microsoft.RTM. .NET or Ruby on Rails (RoR). Optionally, in some
embodiments, a web application utilizes one or more database
systems including, by way of non-limiting examples, relational,
non-relational, object oriented, associative, and XML database
systems. In further embodiments, suitable relational database
systems include, by way of non-limiting examples, Microsoft.RTM.
SQL Server, mySQL.TM., and Oracle.RTM.. Those of skill in the art
will also recognize that a web application, in various embodiments,
is written in one or more versions of one or more languages. A web
application can be written in one or more markup languages,
presentation definition languages, client-side scripting languages,
server-side coding languages, database query languages, or
combinations thereof. Optionally, in some embodiments, a web
application is written to some extent in a markup language such as
Hypertext Markup Language (HTML), Extensible Hypertext Markup
Language (XHTML), or eXtensible Markup Language (XML). Optionally,
in some embodiments, a web application is written to some extent in
a presentation definition language such as Cascading Style Sheets
(CSS). Optionally, in some embodiments, a web application is
written to some extent in a client-side scripting language such as
Asynchronous JavaScript and XML (AJAX), Flash.RTM. ActionScript,
JavaScript, or Silverlight.RTM.. Optionally, in some embodiments, a
web application is written to some extent in a server-side coding
language such as Active Server Pages (ASP), ColdFusion.RTM., Perl,
Java.TM., Java Server Pages (JSP), Hypertext Preprocessor (PHP),
Python.TM. Ruby, Tcl, Smalltalk, WebDNA.RTM., or Groovy.
Optionally, in some embodiments, a web application is written to
some extent in a database query language such as Structured Query
Language (SQL). Optionally, in some embodiments, a web application
integrates enterprise server products such as IBM.RTM. Lotus
Domino.RTM.. Optionally, in some embodiments, a web application
includes a media player element. In various further embodiments, a
media player element utilizes one or more of many suitable
multimedia technologies including, by way of non-limiting examples,
Adobe.RTM. Flash.RTM., HTML 5, Apple.RTM. QuickTime.RTM.,
Microsoft.RTM. Silverlight.RTM., Java.TM., and Unity.RTM..
[0203] Referring to FIG. 5, in a particular embodiment, an
application provision system comprises one or more databases 500
accessed by a relational database management system (RDBMS) 510.
Suitable RDBMSs include Firebird, MySQL, PostgreSQL, SQLite, Oracle
Database, Microsoft SQL Server, IBM DB2, IBM Informix, SAP Sybase,
SAP Sybase, Teradata, and the like. In this embodiment, the
application provision system further comprises one or more
application severs 520 (such as Java servers, .NET servers, PHP
servers, and the like) and one or more web servers 530 (such as
Apache, IIS, GWS and the like). The web server(s) optionally expose
one or more web services via app application programming interfaces
(APIs) 540. Via a network, such as the internet, the system
provides browser-based and/or mobile native user interfaces.
[0204] Referring to FIG. 6, in a particular embodiment, an
application provision system alternatively has a distributed,
cloud-based architecture 600 and comprises elastically load
balanced, auto-scaling web server resources 610, and application
server resources 620 as well synchronously replicated databases
630.
Mobile Application
[0205] Optionally, in some embodiments, a computer program includes
a mobile application provided to a mobile digital processing
device. Optionally, in some embodiments, the mobile application is
provided to a mobile digital processing device at the time it is
manufactured. In other embodiments, the mobile application is
provided to a mobile digital processing device via the computer
network described herein.
[0206] In view of the disclosure provided herein, a mobile
application is created by techniques known to those of skill in the
art using hardware, languages, and development environments known
to the art. Those of skill in the art will recognize that mobile
applications are written in several languages. Suitable programming
languages include, by way of non-limiting examples, C, C++, C#,
Objective-C, Java.TM., JavaScript, Pascal, Object Pascal,
Python.TM., Ruby, VB.NET, WML, and XHTML/HTML with or without CSS,
or combinations thereof.
[0207] Suitable mobile application development environments are
available from several sources. Commercially available development
environments include, by way of non-limiting examples, AirplaySDK,
alcheMo, Appcelerator.RTM., Celsius, Bedrock, Flash Lite, .NET
Compact Framework, Rhomobile, and WorkLight Mobile Platform. Other
development environments are available without cost including, by
way of non-limiting examples, Lazarus, MobiFlex, MoSync, and
Phonegap. Also, mobile device manufacturers distribute software
developer kits including, by way of non-limiting examples, iPhone
and iPad (iOS) SDK, Android.TM. SDK, BlackBerry.RTM. SDK, BREW SDK,
Palm.RTM. OS SDK, Symbian SDK, webOS SDK, and Windows.RTM. Mobile
SDK.
[0208] Those of skill in the art will recognize that several
commercial forums are available for distribution of mobile
applications including, by way of non-limiting examples, Apple.RTM.
App Store, Google.RTM. Play, Chrome Web Store, BlackBerry.RTM. App
World, App Store for Palm devices, App Catalog for webOS,
Windows.RTM. Marketplace for Mobile, Ovi Store for Nokia.RTM.
devices, Samsung.RTM. Apps, and Nintendo.RTM. DSi Shop.
Standalone Application
[0209] Optionally, in some embodiments, a computer program includes
a standalone application, which is a program that is run as an
independent computer process, not an add-on to an existing process,
e.g., not a plug-in. Those of skill in the art will recognize that
standalone applications are often compiled. A compiler is a
computer program(s) that transforms source code written in a
programming language into binary object code such as assembly
language or machine code. Suitable compiled programming languages
include, by way of non-limiting examples, C, C++, Objective-C,
COBOL, Delphi, Eiffel, Java.TM., Lisp, Python.TM., Visual Basic,
and VB .NET, or combinations thereof. Compilation is often
performed, at least in part, to create an executable program.
Optionally, in some embodiments, a computer program includes one or
more executable complied applications.
Web Browser Plug-in
[0210] Optionally, in some embodiments, the computer program
includes a web browser plug-in (e.g., extension, etc.). In
computing, a plug-in is one or more software components that add
specific functionality to a larger software application. Makers of
software applications support plug-ins to enable third-party
developers to create abilities which extend an application, to
support easily adding new features, and to reduce the size of an
application. When supported, plug-ins enable customizing the
functionality of a software application. For example, plug-ins are
commonly used in web browsers to play video, generate
interactivity, scan for viruses, and display particular file types.
Those of skill in the art will be familiar with several web browser
plug-ins including, Adobe.RTM. Flash.RTM. Player, Microsoft.RTM.
Silverlight.RTM., and Apple QuickTime.RTM..
[0211] In view of the disclosure provided herein, those of skill in
the art will recognize that several plug-in frameworks are
available that enable development of plug-ins in various
programming languages, including, by way of non-limiting examples,
C++, Delphi, Java.TM., PHP, Python.TM., and VB .NET, or
combinations thereof.
[0212] Web browsers (also called Internet browsers) are software
applications, designed for use with network-connected digital
processing devices, for retrieving, presenting, and traversing
information resources on the World Wide Web. Suitable web browsers
include, by way of non-limiting examples, Microsoft.RTM. Internet
Explorer.RTM., Mozilla.RTM. Firefox.RTM., Google.RTM. Chrome,
Apple.RTM. Safari.RTM., Opera Software.RTM. Opera.RTM., and KDE
Konqueror. Optionally, in some embodiments, the web browser is a
mobile web browser. Mobile web browsers (also called microbrowsers,
mini-browsers, and wireless browsers) are designed for use on
mobile digital processing devices including, by way of non-limiting
examples, handheld computers, tablet computers, netbook computers,
subnotebook computers, smartphones, music players, personal digital
assistants (PDAs), and handheld video game systems. Suitable mobile
web browsers include, by way of non-limiting examples, Google.RTM.
Android.RTM. browser, RIM BlackBerry.RTM. Browser, Apple.RTM.
Safari.RTM., Palm.RTM. Blazer, Palm.RTM. WebOS.RTM. Browser,
Mozilla.RTM. Firefox.RTM. for mobile, Microsoft.RTM. Internet
Explorer.RTM. Mobile, Amazon.RTM. Kindle.RTM. Basic Web, Nokia.RTM.
Browser, Opera Software.RTM. Opera.RTM. Mobile, and Sony.RTM.
PSP.TM. browser.
Software Modules
[0213] Optionally, in some embodiments, the platforms, systems,
media, and methods disclosed herein include software, server,
and/or database modules, or use of the same. In view of the
disclosure provided herein, software modules are created by
techniques known to those of skill in the art using machines,
software, and languages known to the art. The software modules
disclosed herein are implemented in a multitude of ways. In various
embodiments, a software module comprises a file, a section of code,
a programming object, a programming structure, or combinations
thereof. In further various embodiments, a software module
comprises a plurality of files, a plurality of sections of code, a
plurality of programming objects, a plurality of programming
structures, or combinations thereof. In various embodiments, the
one or more software modules comprise, by way of non-limiting
examples, a web application, a mobile application, and a standalone
application. Optionally, in some embodiments, software modules are
in one computer program or application. In other embodiments,
software modules are in more than one computer program or
application. Optionally, in some embodiments, software modules are
hosted on one machine. In other embodiments, software modules are
hosted on more than one machine. In further embodiments, software
modules are hosted on cloud computing platforms. Optionally, in
some embodiments, software modules are hosted on one or more
machines in one location. In other embodiments, software modules
are hosted on one or more machines in more than one location.
Databases
[0214] Optionally, in some embodiments, the platforms, systems,
media, and methods disclosed herein include one or more databases,
or use of the same. In view of the disclosure provided herein,
those of skill in the art will recognize that many databases are
suitable for storing data from one or more sources related to a
data set and/or a property. In various embodiments, suitable
databases include, by way of non-limiting examples, relational
databases, non-relational databases, object oriented databases,
object databases, entity-relationship model databases, associative
databases, and XML databases. Further non-limiting examples include
SQL, PostgreSQL, MySQL, Oracle, DB2, and Sybase. Optionally, in
some embodiments, a database is internet-based. In further
embodiments, a database is web-based. In still further embodiments,
a database is cloud computing-based. In other embodiments, a
database is based on one or more local computer storage
devices.
Graphic User Interfaces
[0215] Optionally, in some embodiments, the platforms, systems,
media, and methods disclosed herein are presented through one or
more graphic user interfaces.
[0216] FIG. 15 is a non-limiting example of a graphic user
interface 1500. In some embodiments, the graphic user interface
offers an application for viewing publicly available along with
opaque unreported events throughout a property's existence. In some
embodiments, the application provides a visual timeline format that
is easy to read coupled with a comprehensive, line-by-line report.
FIG. 16 is a non-limiting example of the graphic user interface
depicted in FIG. 15 on a laptop 1600. FIG. 17 is a non-limiting
example of a graphic user interface on a desktop 1700. In other
embodiments, the graphic user interface for viewing publicly
available along with opaque unreported events can be displayed in
any transitory storage medium.
[0217] FIG. 18 is a non-limiting example of a graphic user
interface; in this case, an interface for viewing a timeline and
overview of publicly available events throughout a property's
existence 1800. In some embodiments, the graphic user interface
provides a side panel 1801 that provides an overview of a
property's details. In some embodiments, the property details
include the property address, APN/AIN number, type of property
(e.g., single family residential, condo, townhome, multi-unit,
etc.), tax rate area, legal info, year built, effective year built,
physical attributes (e.g., number of bedroom, bathrooms, and baths;
square footage, lot acreage; lot square footage), and roll values
(e.g., recording data, fair market value of land and improvements,
personal property, fixtures, homeowners' exemption, real estate
exemption, personal property exemption, and fixture exemptions),
and a map of the property.
[0218] FIG. 19 is a non-limiting example of a graphic user
interface; in this case, an interface for viewing a timeline and
overview of publicly available events throughout a property's
existence 1900. In some embodiments, the graphic user interface
offers a REPORTED mode 1901, wherein a user can select a node 1902
on a timeline of reported events for a property of interest. In
some embodiments, selecting a node 1902 will display information
about the reported event 1903. By way of example, a reported event
can comprise a transfer of deed. In such an example, additional
information about the reported event can include the recorded data
of the deed, document number, sale price, sale type, title company,
buyer, and seller.
[0219] FIG. 20 is a non-limiting example of a graphic user
interface; in this case, an interface for viewing a timeline and
overview of opaque unreported events throughout a property's
existence 2000. In some embodiments, the graphic user interface
offers an UNREPORTED mode 2001, wherein a user can select a node
2002 on a timeline of unreported events for a property of interest.
In some embodiments, selecting a node 2002 will display additional
information about the unreported event 2003. By way of example, an
unreported event can comprise of a permit--public right of way. In
such an example, additional information about the unreported can
include the filing date, document type, document number, source,
permit fee, work start and work end dates, street work, cross
street, applicant name, contractor name, and whether the contractor
was licensed. In some embodiments, the unreported event comprises
the unpermitted renovation events, improper real estate transfer
event, or any combination thereof.
[0220] FIG. 21 is a non-limiting example of a graphic user
interface; in this case, an interface for simultaneously viewing a
timeline of publicly available along with opaque unreported events
throughout a property's existence 2100. In some embodiments, the
graphic user interface offers a COMPARE mode 2101, wherein a user
can simultaneously view and compare a timeline of publicly
available events throughout a property's existence 2102 and a
timeline of opaque unreported events throughout the same property's
existence 2103. In some embodiments, the timelines are linked so
that a user scrolling up and down the interface will result in both
timelines being scrolled through simultaneously. In some
embodiments, the timeline of publicly available events throughout a
property's existence 2102 comprises information known about a home.
In some embodiments, the timeline of opaque unreported events
throughout the same property's existence 2103 comprises unreported
information relevant to identify when a home has been altered,
potentially without proper permits. In some embodiments, events
that span a time period rather than a specific date is portrayed
through long bubbles rather than a single node. In some
embodiments, events that span a time period of a specific data
comprises events where suspected alterations were made to a home
and not reported through a standard permit process. Optionally, in
some embodiments, a plurality of timelines are provided and
compared. Optionally, in some embodiments, a third timeline of
unreported events with hard documentary evidence (e.g.,
construction) can be provided. In some embodiments, all the
plurality of timelines are linked so that a user scrolling up and
down the interface will result in all timelines being scrolled
through simultaneously.
[0221] FIG. 22 is a non-limiting example of a graphic user
interface; in this case, an interface for viewing and sorting
records associated with a property of interest 2200. In some
embodiments, a user can select a PERMIT module 2201. In some
embodiments, line-by-line records can be viewed, sorted, and
modified on the data grid 2202.
[0222] FIG. 23 is a non-limiting example of a graphic user
interface; in this case, an interface for viewing images of the
property interest 2300. In some embodiments, unreported items can
store the latest images of a property's listing 2301.
[0223] FIG. 24 is a non-limiting example of a graphic user
interface; in this case, a module for toggling the timeline view
2401. In some embodiments, the timeline can be presented in a
horizontal view. In other embodiments, the timeline can be
presented in a vertical view.
Terms and Definitions
[0224] Unless otherwise defined, all technical terms used herein
have the same meaning as commonly understood by one of ordinary
skill in the art to which this disclosure belongs.
[0225] As used herein, the singular forms "a," "an," and "the"
include plural references unless the context clearly dictates
otherwise. Any reference to "or" herein is intended to encompass
"and/or" unless otherwise stated.
[0226] As used herein, the term "about" refers to an amount that is
near the stated amount by 10%, 5%, or 1%, including increments
therein.
[0227] As used herein, the term "natural language task process"
refers to a computer process of configured to efficiently and
accurately recognize contextual information from natural language
data.
EXAMPLES
[0228] The following illustrative examples are representative of
embodiments of the software applications, systems, and methods
described herein and are not meant to be limiting in any way. While
preferred embodiments of the present disclosure have been shown and
described herein, it will be obvious to those skilled in the art
that such embodiments are provided by way of example only. Numerous
variations, changes, and substitutions will now occur to those
skilled in the art without departing from the disclosure. It should
be understood that various alternatives to the embodiments of the
disclosure described herein can be employed in practicing the
disclosure.
Example 1--Detection of an Unpermitted Renovation by a
Corporation
[0229] In a first example herein, a renovation detection module
receives a list of known flipper corporations from the database
comprising a list of known "flipper" corporations. When the
renovation detection module receives an indication that the square
footage of the building on 123 Main Street has increased by 250
square feet from an MLS external data source, and as no permit has
been requested for that address, it identifies that 123 Main Street
is an initial candidate property with a baseline unpermitted
renovation probability of 50%. The renovation probability module
determines that 123 Main Street was recently bought by Corporation
A and increases the probability that the change in the square
footage of 123 Main Street is due to an unpermitted renovation to
55%. Further, because Corporation A is a known "flipper"
corporation, the unpermitted renovation probability is again
increased to 60%. As the unpermitted renovation probability for 123
Main Street is above the threshold of 55%, the property is further
screened.
[0230] Since it was determined that 123 Main Street is owned by
Corporation A, the renovation probability module further determines
that its corporate officers are Ben and Charlie and checks their
social media for indicia of renovations. As both Ben and Charlie
have posted construction pictures to social media the renovation
probability is increased to 65%. Further, because Ben, as a
corporate officer of Corporation A, has been previously associated
with flipping houses, the renovation probability is additionally
increased to 70%. By reviewing data from MLS and other sources the
renovation probability module further confirms that renovations
have or are occurring at 123 Main Street, and the renovation
probability is additionally increased to 70%. With a final
renovation probability of 70%, above the T2 threshold of 65%, an
indication of a highly probable unpermitted renovation, the
renovation probability module sends an instruction to the candidate
validation module to inspect 123 Main Street.
[0231] City officials visit 123 Main Street to determine that
renovations are being made, and issue a warning or fine for the
unpermitted renovations. The candidate validation module then sends
an indication to the renovation probability module and the
renovation detection module to validate the learning and processing
parameters therein.
Example 2--Detection of an Unpermitted Renovation by an Owner
[0232] In a second example herein, a renovation detection module
receives an indication that a social media image with the
description "Brand New Carport!" was tagged at 123 Birch Lane, and
as no permit has been requested for that address, it identifies
that 123 Birch Lane is an initial candidate property with a
baseline unpermitted renovation probability of 55%. The renovation
probability module determines that 123 Birch Lane was recently
bought by Jane and Jim, who have no previous history of house
flipping or unpermitted renovations, and maintains the unpermitted
renovation probability of 55%. As the unpermitted renovation
probability for 123 Birch Lane meets the threshold of 55%, the
property is further screened.
[0233] Since it was determined that 123 Birch Lane is owned by Jane
and Jim, the renovation probability module checks other social
media sources for posts by Jane and Jim, determines that both have
displayed renovation and/or construction related information and
increases the renovation probability to 62.5%. With a renovation
probability of 62.5%, an indication of a moderately probable
unpermitted renovation, below the T2 threshold of 65%, the
renovation probability module then determines that the renovation
probability is greater than the T3 probability of 60% and searches
for further evidence from social media, MLS, and other sources. As
further research of the MLS listing shows that the property has
been put up for sale, the unpermitted renovation probability is
increased to 67.5% and above the T2 threshold of 65%. The
renovation probability module then sends an instruction to the
candidate validation module to inspect 123 Birch Lane Street.
[0234] City officials visit 123 Birch Lane Street to determine that
renovations are being made, and issue a warning or fine for the
unpermitted renovations. The candidate validation module then sends
an indication to the renovation probability module and the
renovation detection module to validate the learning and processing
parameters therein.
Example 3--Assignment of Inspectors to Properties
[0235] In a third example herein, the machine learning and
filtering engine recommends that an inspector be sent to properties
at 111 A Street and at 222 B Street. Current renovation indicia
shows that 111 A Street and 222 B Street were sold within the last
two months and the last two years, respectively, and that the owner
of 111 A Street has recently flipped houses. Social media shows
that the owner of 111 A Street posted images yesterday of a new
refrigerator appliance. Further, as recent street view images of
111 A Street show a mostly complete extension not seen in the
original MLS listing, the renovation on that property is estimated
to conclude within one month. Social media images by the owners of
222 B Street with bags of concrete and waste bins and recent wood
purchases indicate that the renovations at that address will
conclude in at least 4 months. Inspector C, who lives closest to
111 A Street, is assigned to inspect that property immediately.
Inspector W, who has a free schedule next month, is assigned to
inspect 222 B Street within that period of time.
Example 4--Assignment of a Plurality of Inspectors to
Properties
[0236] In a fourth example herein the city of Flipville has 5
inspectors, and they can typically visit 3 sites per day. 200
active, unpermitted, and uninspected renovations are determined
from data mining techniques. The various data sources are further
examined to estimate how close that property is to finishing the
renovation, wherein a value F corresponding to the estimated days
to completion is assigned to each property. The list is then sorted
from smallest to largest F, whereby the first property is assigned
to the first inspector. The inspector can be selected randomly, or
based on where the inspector lives, closer to the property being
better, assuming that they go directly to the property at the start
of the working day, or other criteria. A formula, such as one
maximizing 1/(F+D) is used to select another 4 properties for that
inspector to visit that day, whereby grouping the properties
geographically allows the inspector to visit more properties per
day. The assigned properties are removed from the list and the
process is repeated for the remaining inspectors, thus assigning 25
properties to be inspected. This process is repeated each day, with
newly discovered properties being added to the list, inspected
properties being removed and data from the inspection being
incorporated into the learning algorithms, and the list being
re-sorted and re-processed.
Example 5--Detecting an Improper Real Estate Transfer Event
[0237] In a fifth example herein brothers A, B, and C were each
bequeathed a third of the property at 333 D Street, valued at 30
million dollars. The improper transfer detection module determines
that 333D street is a potential candidate once the data ingestion
interface receives a real estate transfer indicia related to the
bequeathment through a sales trust, the value of the property, and
a real estate transfer indicia comprising a sale by brother A of
his portion of the real estate. The improper real estate transfer
probability calculation module then determines that the initial
candidate has a high probability of an improper real estate
transfer, as the sale occurred through a sales trust, and as
brother A sold his 1/3.sup.rd share, valued at 10 million dollars,
to brother C for only 1 million dollars. An inspection of the
highly probable initial candidate finds that the 30 million dollar
valuation is accurate, and that as such, an improper real estate
transfer by brother A has occurred.
Example 6--Detecting an Improper Real Estate Transfer Event Using
Historical Data
[0238] In a sixth example herein brothers D, E, and F were each
bequeathed a third of the property at 444 G Street. The ingestion
interfaces receive notice of the bequeathment and the proper taxes,
fees, and forms filled out by each brother for their respective
share of the property, and store this information in the historical
transfer database. One year later brother D sells his 1/3.sup.rd
share to brother F, but because less than 50% of the ownership of
the property has changed, this change of ownership is not reported
and a re-appraisal is not performed. The sale by brother D is
appended to the previously stored data regarding 444 G Street in
the historical transfer database. Subsequently brother E also sells
his 1/3.sup.rd share to brother F. Brother D did not report the
change in ownership or request a re-appraisal during his sale. Once
the data ingestion interfaces detect the sale by brother E of his
1/3.sup.rd share, the improper transfer detection module recalls
the previous sale by brother D of his 1/3.sup.rd share (i.e.,
remembers the sequence of transfers), and determines that, as the
change in ownership is now greater than 50%, an improper transfer
has occurred.
Example 7--Determining when One or More Unpermitted Renovation
Events has Taken Place
[0239] In a seventh example herein 123 B Street is determined to be
a candidate of an unpermitted renovation. The set of first data
ingestion interfaces determines that as the last government
documented assessment of 123 B Street on January 2012 does not
include the unpermitted renovation, that the renovation must have
occurred since then. The set of second data ingestion interfaces
determines the unpermitted renovation timing indicia that a
dumpster delivery request was made for two dumpsters to the address
of the candidate location in February 2001 and that a manufacturer
warrantee is documented as citing a refrigerator installation date
in June 2013. The renovation timing estimation module then applies
a machine learning algorithm to present a refined renovation time
range of between March 2001 and June 2013 based on the detected
initial time range and the detected unpermitted renovation timing
indicia. An administrative user then employs a set of third data
ingestion interfaces which determines that the owner of 123 B
Street ordered a carpet installation in April 2002 and granite
installation in March of 2013. The renovation timing estimation
module then applies a machine learning algorithm to determine that,
based on the unpermitted renovation timing indicia from the set
third of third data ingestion interfaces two renovations took place
from February 2001 and June 2013; a bedroom addition within a
further refined renovation time range of March 2001 and April 2002
and a kitchen remodeling within a further refined renovation time
range of February of 2013 and June 2013. The owners are then
charged the appropriate back taxes for the difference in property
value since March 2001.
Example 8--Detecting an Improper Residency Status
[0240] In an eighth example herein Bob has a primary residence in
Minnesota and a vacation home in California. Upon his retirement,
Bob decides to move permanently to a warmer climate, but to keep
his Minnesota residence for visiting his family. Bob continues to
file interest deductions on his tax returns for his Minnesota
property for the amount commensurate with a primary residence. The
improper residency detection module that identifies Bob's Minnesota
house as an initial candidate based on an improper residency
indicia of a significant increase in his California utility bill
and a significant decrease in his Minnesota utility bill. The
residency probability calculation module then calculates a high
probability of an improper residence status. After inspection and
confirmation of the change of Bob's primary and vacation residence,
the confirmation is feed back to the improper residency probability
calculation module to improve its calculation over time.
* * * * *