U.S. patent application number 14/693574 was filed with the patent office on 2016-10-27 for data collection, storage, and processing system using one or more inputs.
This patent application is currently assigned to Alpha Endeavors LLC. The applicant listed for this patent is Alpha Endeavors LLC. Invention is credited to Dane Christian Jessen.
Application Number | 20160314545 14/693574 |
Document ID | / |
Family ID | 57147802 |
Filed Date | 2016-10-27 |
United States Patent
Application |
20160314545 |
Kind Code |
A1 |
Jessen; Dane Christian |
October 27, 2016 |
DATA COLLECTION, STORAGE, AND PROCESSING SYSTEM USING ONE OR MORE
INPUTS
Abstract
A system for collecting, storing, and/or analyzing data received
from one or more inputs comprising a property information database,
a profile preference database, and one or more modules for
accessing the databases. The system may receive data from a
multiple listing service, internet source, and/or drone. The system
may calculate and store a predicted property value and/or compare
the predicted property value to a stored listed property price. A
real estate offer and/or orders for an inspection, appraisal,
contractor walkthrough, preliminary title report, or loan
applications may be generated and/or sent to an entity based, at
least in part, on information stored in the property information
database and/or the profile preference database.
Inventors: |
Jessen; Dane Christian;
(Tacoma, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Alpha Endeavors LLC |
Tacoma |
WA |
US |
|
|
Assignee: |
Alpha Endeavors LLC
|
Family ID: |
57147802 |
Appl. No.: |
14/693574 |
Filed: |
April 22, 2015 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 50/16 20130101;
G06Q 30/0278 20130101 |
International
Class: |
G06Q 50/16 20060101
G06Q050/16; G06Q 30/02 20060101 G06Q030/02 |
Claims
1. A method of generating real estate offers, the method
comprising: establishing a profile comprising a plurality of
predetermined threshold values; receiving, from a source, a
collection of property metrics related to a property; determining a
subset of the collection of property metrics based, at least in
part, on the profile; calculating a predictive property value
based, at least in part, on a correlating of each property metric
of the subset to a corresponding one of the predetermined threshold
values; generating an offer to purchase the property; and sending
the offer to an entity associated with the profile.
2. The method of claim 1, wherein at least one of the predetermined
threshold values corresponds to at least one of the property
metrics.
3. The method of claim 1, wherein each property metric of the
subset corresponds to at least one of the predetermined threshold
values.
4. The method of claim 1, wherein the generating the offer is in
response to an occurrence of an event related to the property.
5. The method of claim 4, wherein the event comprises a listing
being posted on a real estate listing service, a post on a social
media website, or a publication of a news article.
6. The method of claim 1, wherein the offer forms a part of a
purchase and sale agreement agenda.
7. The method of claim 1, further comprising obtaining a provided
property value; and comparing the predictive property value to the
provided property value.
8. The method of claim 1, wherein the profile comprises at least
one of: geographic information; or a preference for residential or
commercial property.
9. The method of claim 1, wherein the source comprises an
electronic listing service, manually entered information, or a web
analytics API.
10. The method of claim 1, wherein the source comprises a first
source, and further comprising, prior to the calculating a
predictive property value, requesting a property metric from a
second source.
11. A method of collecting real estate information comprising:
receiving multiple information packages from a first source, each
information package corresponding to a respective property;
identifying particular property metrics of the multiple real estate
information packages to consider; generating a subset of properties
for further consideration by comparing the particular property
metrics of each real estate information package to a predetermined
threshold of each property metric; determining a property metric
absent from the subset of properties for further consideration; and
requesting the absent property metric from a second source.
12. The method of claim 11, wherein the second source comprises a
dispatched drone, a social media website, a listing service, a
mechanical turk, an automated telephone polling service, or a web
analytics API.
13. The method of claim 11, wherein the second source comprises a
drone sent to at least one of the properties of the subset of
properties for further consideration.
14. The method of claim 11, wherein the information determined to
be absent comprises information that forms a part of a property
value calculation.
15. The method of claim 11, wherein the identifying is based, at
least in part, on a previously established profile setting.
16. A method of using a drone to gather real estate valuation
information: receiving a request to survey a property, the request
including location information of the property, and property
value-affecting information to be collected by the drone; traveling
to a location associated with the location information; and
collecting the property value-affecting information.
17. The method of claim 16, wherein the collecting comprises
directing a sensor of the drone towards a property feature
associated with the property value-affecting information.
18. The method of claim 16, further comprising receiving a security
key to provide the drone access to an interior space of the
property.
19. The method of claim 18, wherein the security key is configured
to communicatively couple with a security device of the property to
provide the access.
20. The method of claim 16, further comprising calculating a
predictive property value based, at least in part, on the collected
property value-affecting information.
21. The method of claim 16, further comprising sending the property
value-affecting information to a computing device.
Description
BACKGROUND
[0001] Real estate brokers spend substantial amounts of time and
resources collecting and analyzing information about properties so
they can price and market the properties accurately. Much of the
property information a real estate broker collects as it prepares
to sell a property is uploaded to a multiple listing service (MLS),
so that it can be shared with other brokers representing potential
buyers. For instance, property information may be uploaded to the
California Regional Multiple Listing Service (CRMLS), the
Metropolitan Regional Information System (MRIS), and/or other
services using Internet Data Exchange (IDX) or the Real Estate
Transaction Standard (RETS).
[0002] However, real estate is unlike other major asset classes
because each piece of property is unique. Furthermore, potential
buyers can be equally idiosyncratic, each having their own
histories, needs, biases, and intentions for purchasing property.
The unusual nature of real estate transactions often leads to
notoriously "high-friction" costs, meaning the transaction itself
has a multitude of costs and fees associated with it, e.g.,
brokerage fees and commissions, research costs, time, etc.
[0003] It can be difficult for brokers to anticipate what
information will satisfy each potential buyer's needs, so real
estate brokers would like as much information as possible about the
properties they are attempting to sell. However, the time spent
physically examining properties can be expensive. Therefore,
brokers are often forced to limit the amount of information they
collect and risk failing to satisfy a potential buyers' property
information needs with the immediacy that potential buyers
require.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] The detailed description is set forth with reference to the
accompanying figures. In the figures, the left-most digit(s) of a
reference number identifies the figure in which the reference
number first appears. The use of the same reference number multiple
times may indicate a feature that may be singular or plural. The
use of a different reference number in a different figure may
indicate a similar or identical item.
[0005] FIG. 1 illustrates an example system configured to collect,
store, and/or analyze data from one or more inputs.
[0006] FIG. 2 illustrates an example system, which may form a
portion of the system of FIG. 1, including a property information
database and a property valuation module.
[0007] FIG. 3 illustrates an example system, which may form a
portion of the system of FIG. 1, including a profile preference
database and a real estate offer generation module.
[0008] FIG. 4 illustrates an example system, which may form a
portion of the system of FIG. 1, including a data supplementation
module and one or more inputs.
[0009] FIG. 5 illustrates an example system, which may form a
portion of the system of FIG. 1, including a drone.
[0010] FIG. 6 illustrates an example system, which may form a
portion of the system of FIG. 1, including a drone and a physical
property.
[0011] FIG. 7 illustrates an example system, which may form a
portion of the system of FIG. 1, including a drone and a security
door.
[0012] FIG. 8 illustrates an example system, which may form a
portion of the system of FIG. 1, including one or more drones and a
geographic region.
DETAILED DESCRIPTION
Overview
[0013] Real estate transactions are "high-friction" transactions
due to a vast amount of information and circumstances that can
affect real estate values, and potential buyers, or potential
securitizers of the property. This disclosure is directed to a
system which, in some examples, may collect, store, and/or process
information related to physical properties (e.g., residential,
commercial, public, governmental, undeveloped land, partially
developed land, parking lots, and/or combinations thereof), such as
properties for sale or potentially for sale, for reasons of
valuation, to reduce the costs and time associated with real estate
transactions, inspections, or appraisals.
[0014] In some examples, the system receives data from one or more
inputs and stores the data in one or more memories. The system
determines which inputs and/or how many inputs from which to
receive further data based, at least in part, on data already
received in real-time, and automatically updates or changes the
inputs. In some embodiments, due to the collecting, processing
and/or storing of the data, the system provides a manner for
accessing the data that is real-time and/or unique, meaning that
each occurrence of accessing the data provides an output to a user
that is specific to the entity accessing the data, and specific to
a time at which the data is accessed (i.e., is "up-to-date"). In
some examples, the system generates populated documents (such as a
Purchase and Sale Agreement PDF), and sends the document in an
automated, real-time manner, with the most up-to-date information
available. The system reduces redundancy by selectively changing
inputs, seeking the most relevant/important data while omitting
unnecessary data. For at least some of these reasons, some
embodiments of the system provides increased user efficiency and/or
increased user interaction performance.
[0015] In some examples, the system may comprise one or more
modules (i.e., software functionalities stored in the memory)
which, when executed may access and/or use information stored in
one or more databases. For instance, the system may comprise a
property information database that stores information related to
one or more physical properties (e.g., a location, square footage,
year built, etc.). The property information database may receive
property information from one or more sources, such as a multiple
listing service (MLS). A data supplementation module may provide
information to the property information database, such as
information collected from the internet via web analytics API
and/or from a drone that visits the physical property. In some
instances, the drone may be dispatched to collect missing
information, to verify information about the physical property,
gather previously uncollected data, map the property in three
dimensions, and/or to provide a real-time virtual experience of the
physical property.
[0016] In some instances, the data supplementation module (and/or
other parts of the system) may provide information to the property
information database. The information of the property information
database may be used by a property valuation module to calculate a
predicted value of the physical property (e.g., using linear
regression analysis). The property valuation module may value the
property as it currently stands (as-is), or as an estimation of
value due to potential changes made to the property (to-be). The
system may calculate a predicted value and may store the predicted
value, as well as a listed price (for instance, that may be
received from the MLS), and may compare the predicted value to the
listed price. In some instances, the system may generate and/or
send an offer to purchase the property based, at least in part, on
the comparison of the predicted value to the listed price. By
collecting information from multiple inputs/sources, the system may
provide an improved predicted value, which may provide an improved
offer, and the system may provide the offer with an immediacy often
lacking from the real estate market.
[0017] The system may calculate rehabilitation costs and/or compare
the rehabilitation costs to a potential value of the property. The
system may calculate a cost to increase a value of the property
from a bottom quartile to an upper quartile of value, with respect
to other properties in a same geographic region. The system may
identify a property with a high potential value but a low current
value (which may be reflected in a listed price). In some examples,
the system may include a profile preference database (which may
store one or more profiles associated with one or more entities,
such as a real estate brokerage or investment firm) to provide
information about what property metrics should be collected,
stored, and/or analyzed.
[0018] In some instances, the system may provide more abundant,
relevant information in a timelier manner than some real estate
brokers and/or property valuation services may be able to provide.
The system may used at least for buying property, selling property,
calculating a mortgage, insurance appraisals, inspections,
landscaping bids, refinancing, calculating remodeling costs, taking
inventory of a securities portfolio, and/or calculating property
valuations for tax purposes.
[0019] Multiple and varied example implementations and embodiments
are described throughout. However, these examples are merely
illustrative and other implementations and embodiments of the
system may be implemented without departing from the scope of the
disclosure. For instance, the implementations, or portions thereof,
may be rearranged, combined, used together, duplicated, partially
omitted, omitted entirely, and/or may be otherwise modified to
arrive at variations on the disclosed implementations.
Illustrative Data Collection, Storage, and/or Analysis System
[0020] FIG. 1 illustrates an example environment or system 100
configured to collect, store, and/or analyze information from one
or more inputs. In some instances, the information may comprise
property information which relates to one or more physical
properties (hereafter referred to as "the property 102") and/or a
value of the property 102. In some examples, the system 100 may
generate an output, such as an offer to purchase the property 102
based, at least in part, on the property information. The property
information may comprise one or more pieces of data related to the
property 102. In some instances, the property information may
comprise one or more characteristics/features that may affect a
value of the property 102. In some examples, the property
information may be associated with a timestamp such that the system
100 may collect, store, update, and/or analysis the property
information in a real-time matter.
[0021] In some examples of the techniques discusses herein, the
methods of operation may be performed by one or more application
specific integrated circuits (ASIC) or may be performed by a
general purpose processor utilizing software defined in computer
readable media. In the examples and techniques discussed herein,
the memory device may comprise computer-readable media and may take
the form of volatile memory, such as random access memory (RAM)
and/or non-volatile memory, such as read only memory (ROM) or flash
RAM. Computer-readable media devices include volatile and
non-volatile, removable and non-removable media implemented in any
method or technology for storage of information such as
computer-readable instructions, data structures, program modules,
or other data for execution by one or more processors of a
computing device. Examples of computer-readable media include, but
are not limited to, phase change memory (PRAM), static
random-access memory (SRAM), dynamic random-access memory (DRAM),
other types of random access memory (RAM), read-only memory (ROM),
electrically erasable programmable read-only memory (EEPROM), flash
memory or other memory technology, compact disk read-only memory
(CD-ROM), digital versatile disks (DVD) or other optical storage,
magnetic cassettes, magnetic tape, magnetic disk storage or other
magnetic storage devices, or any other non-transitory medium that
can be used to store information for access by a computing
device.
[0022] As defined herein, computer-readable media does not include
transitory media, such as modulated data signals and carrier waves,
and/or signals.
[0023] In some embodiments, the system 100 may include one or more
software functionalities configured as one or more modules, which
may be stored in one or more memories ("the memory 104") at a
single location or in multiple distributed locations (e.g., "cloud
computing/storage"). For instance, the modules may be stored in the
memory 104 of a computing device 106, such as a central office,
datacenter, server room, desktop computer, laptop computer, mobile
device (e.g., smart phone, smart watch, and/or other wearable
device), drone, automotive, and/or smart home with computing
capabilities. One or more of the computing device/s may include a
processor 108, the memory 104, and/or an operating system 110
stored on the memory 104. The modules may be stored in the memory
104 as processor-readable instructions, which when executed by the
processor 108 configure the computing device 106 to perform
functions that may include some or all of the functions described
herein. In some instances, the system 100 may comprise a parallel
processing system using multiple processors to process distributed
portions of data which are re-aggregated.
[0024] The modules described herein are intended to represent
example divisions of the software for purposes of discussion, and
are not intended to represent any type of requirement or required
method, manner, or necessary organization. Accordingly, while
various "modules" are discussed, their functionality and/or similar
functionality could be arranged differently (e.g., combined into a
fewer number of modules, broken into a larger number of modules,
etc.). In some instances, one or more databases may be stored in
the memory 104. The databases discussed herein may have alternative
arrangements (e.g., combined into fewer number of databases, broken
into a larger number of databases, etc.).
[0025] In some examples, the system 100 may comprise a property
information database 112 that may store the property information.
The property information database 112 may receive the property
information from one or more multiple listings services ("the MLS
114"), such as the California Regional Multiple Listing Service
(CRMLS), the Metropolitan Regional Information System (MRIS),
and/or other services. In some instances, the property information
database 112 may receive information from any source using Internet
Data Exchange (IDX), Real Estate Transaction Standard (RETS), File
Transfer Protocol (FTP), or any other data standards or formats
such as a XML or YAML data feed, the JavaScript Object Notation
(JSON) message standard, the Binary JSON (BSON) message standard, a
Simple Object Access Protocol (SOAP) protocol or Representational
State Transfer (REST) protocol. In some examples, the property
information database 112 may comprise an update schedule that
determines a time or multiple times at regular or irregular
intervals that the property information database 112 receives the
property information from the MLS 114. For instance, the property
information database 112 may receive property information from the
MLS 114 about every second, five seconds, 10 seconds, 15 seconds,
30 seconds, minute, five minutes, 10 minutes, 20 minutes, 30
minutes, hour, two hours, four hours, bi-daily, daily, bi-weekly,
and/or weekly. The property information database 112 may receive
property information from the MLS 114 about every 0.1-0.5 seconds,
0.1-1 seconds, 1-2 seconds, 2-5 seconds, 5-10 seconds, 1-2 minutes,
2-5 minutes, 5-10 minutes, 10-20 minutes, 30-60 minutes, 0.1-1
hours, 0.5-1 hours, 1-2 hours, 2-4 hours, 4-8 hours, 12-24 hours,
1-2 days, etc. In some instances, the property information database
112 may receive property information from the MLS 114 responsive to
an occurrence of an event, such as a property being put on the
market. In some examples, the property information may comprise one
or more values corresponding to one or more property metrics.
[0026] In some embodiments, the property information database 112
may request and/or receive property information from a data
supplementation module 116. The data supplementation module 116 may
request and/or receive property information from the MLS 114 and/or
from a source that is different than the MLS 114, such as a network
118 (e.g., an internet or intranet) and/or a drone 120. The request
may be generated and/or sent in response to an occurrence of an
event, by identifying missing, and/or by "high-interest,"
"desired," or "attention-needed" property information.
[0027] In some examples, the system 100 may comprise a profile
preference database 122. The profile preference database 122 may
request, receive, and/or store profile information that correspond
to one more profiles ("the profile") associated with one or more
entities ("the entity"). The entity may comprise a real estate
broker, a real estate brokerage firm, a potential real estate
buyer/seller, an individual person, a building supplies company, a
survey entity (e.g., census data collector), an insurance company,
a hedge fund, a private equity fund, and/or any other businesses,
people, or organizations involved or interested in the information
of real estate markets. In some instances, the profile preference
database 122 may receive the profile information from a client
device 124, which may comprise any of the computing devices
discussed above. For instance, the entity may use the client device
124 to submit profile information to the profile preference
database 122 over the network 118. Additionally or alternatively,
the profile preference database 122 may receive profile information
from another source, such as information received by a phone call,
through traditional mail ("snail mail"), a text message, an instant
message, an email, and/or information received from another
database that stores entity-specific information, such as a
database of a social media site, a real estate information web
service (e.g., Zillow.RTM.), and/or a data brokerage firm.
[0028] In some embodiments, the profile information stored in the
profile preference database 122 may comprise one or more threshold
values (hereafter referred to as "the threshold value 126"). The
threshold value 126 may comprise data indicating a preference of
the profile corresponding to the entity for the property metric
value. For instance, the property metric may comprise measurable or
otherwise determinable features of the property 102 (e.g., square
feet, year built, proximity to a school, etc.), or its owners or
inhabitants, which, in some instances, may affect a value of the
property 102. The property metric value may comprise a measured or
determined value (number, quantity, or characteristic) of the
property metric for the property 102. The threshold value 126 may
comprise a preference or limit for the property metric value of the
entity of the profile.
[0029] In some examples, the profile preference database 122 may
comprise a "high-interest" property list 128. For instance, the
"high-interest" property list 128 may store associations of the
profile to one or more properties identified as "high-interest"
properties. Although the terminology "high-interest" is used in
this application, the "high-interest" property list 128 may
comprise a "desired" property list, a "predetermined" property
list, an "indicated" property list, a "selected" property list, a
"significant" property list, "attention-needed" property, and/or
the like. In some examples, the "high-interest" property list 128
may comprise a subset of the one or more properties 102 that are
determined to more relevant to the profile than other properties of
the one or more properties 102. A classification of the property
102 as a "high-interest" property may be determined by comparing
the threshold value 126 of the profile to one or more of the
property metric values associated with the property 102.
Additionally or alternatively, the property 102 may be classified
as a "high-interest" property based, at least in part, on other
information, such as an indication by the entity associated with
the profile that the entity would like to monitor the property 102,
or by information collected by a web analytics API that determines
an association between the property 102 and the entity.
[0030] In some embodiments, the system 100 may comprise a property
valuation module 130. The property valuation module 128 may receive
and/or access property information stored in the property
information database 112. Additionally or alternatively, the
property valuation module 130 may receive and/or access property
information from any of the sources discussed above that may
provide property information to the property information database
112. In some examples, the property valuation module 130 may apply
a regression analysis, such as a linear regression, logistic
regression, probabilistic matching, artificial neural networks,
support vector machines, statistical clustering, and/or other forms
of classification and/or regression analysis to calculate one or
more predicted property values from at least the one or more
property metric values. In some instances, the predicted property
value generated by the property valuation module 130 may be stored
in the property information database 112.
[0031] In some examples, the system 100 may comprise a real estate
offer generation module 132. The real estate offer generation
module 132 may communicate with and/or access the profile
preference database 122, the threshold value 126, the high-interest
property list 128, and/or the predicted property value generated by
the property valuation module 130. In some instances, the real
estate offer generation module 132 may comprise a threshold
comparison module which may compare the property metric value of
the property 102 to the corresponding threshold value 126 to
determine if the property metric value meets, exceeds, and/or falls
short of the threshold value 126.
[0032] In some embodiments, the real estate offer generation module
132 may comprise a Purchase and Sale Agreement application program
interface (API) which may populate one or more fields of a Purchase
and Sale Agreement document. In some examples, information to
populate the one or more fields may be accessed and/or derived from
the threshold comparison module, the property valuation module 130,
the property information database 112, and/or the profile
preference database 122. In some instances, the real estate offer
generation module 132 may make a determination to send a real
estate offer to the entity associated with the profile. In some
examples, a populated or partially populated Purchase and Sale
Agreement may be sent from the real estate offer generation module
132 to the entity associated with the profile, for instance, in
response to a property metric value meeting a threshold value 126,
in response to a property metric value exceeding a threshold value
126 by a predetermined amount, and/or in response to a
predetermined number or set of threshold values 126 being met
and/or exceeded. In some instances, the Purchase and Sale Agreement
may be partially populated by the real estate offer generation
module 132 and/or partially populated by another module and/or
entity. In some examples, (e.g., such as when data of one or more
fields is unavailable) the field may be populated with a fillable
data field enabling a user to supplement the missing data upon
receiving the Purchase and Sale Agreement. The Purchase and Sale
Agreement may be customizable to meet a specification indicated by
the entity. For example, the Purchase and Sale Agreement may
include the entity's company logo, slogan, or other branding
parameters.
[0033] FIG. 2 illustrates an example environment or system 200
configured to collect, store, and/or analyze information from one
or more inputs. In some instances, the system 200 may comprise a
portion of the system described above. The system 200 may comprise
a property information database 202 that receives and stores
information related to one or more properties. The system may
comprise a property valuation module 204 for calculating a
predictive property value.
[0034] In some embodiments, the property information database 202
may comprise one or more spreadsheets 206 and/or other data
structures that store information specific to each of one or more
properties. The spreadsheet 206 may comprise a plurality of rows
208 and a plurality of columns 210. A first row may comprise a
header row 212 to describe a type of information stored in each
column 210, such as describing one of the columns 210 as a property
identity column 214. Each field of the property identity column 214
may include information to identify a physical property, the rest
of the information of that respective row pertaining to the
identified physical property. For instance, a second row 216 may
include an address, such as "40 E Hart Rd Spokane, Wash." in the
property identity column. Therefore, the remaining fields (i.e.,
columns) of the second row 216 are associated with the physical
property identified as "40 E. Hart Rd Spokane, Wash.".
[0035] In some instances, the property identity column 214 may
comprise a property address, a property GPS location, a property
name (e.g., "The Paulson Building"), an assigned property ID code
(e.g., "2321-WA") or any other name, symbol, or piece of data to
indicate a physical property with which the respective row of
property information is associated. Additionally or alternatively,
location information (e.g., GPS coordinates, address, etc.) may
comprise a different one of the columns 210 than the property
identity column 216.
[0036] In some embodiments, a second column 218 of the spreadsheet
206 may comprise a property metric, such as "number of bedrooms".
Each field in the second column 218 may comprise a property metric
value indicating a number of bedrooms of a property associated with
a one of the rows 208 (e.g., a property identified in the property
identity column 214). By way of example, the second row 216 may
include a number "3" in the second column 218, indicating that the
property of the second row 216 (e.g., the "40 E. Hart Rd Spokane,
Wash." property) comprises 3 bedrooms.
[0037] In some examples, the spreadsheet 206 may comprise a third
column 220, a fourth column 222, or any number N columns, each
column corresponding to a property metric. In some examples, each
column of the N columns may correspond to a measurable or
identifiable feature or characteristic of a physical property, such
as a condition of a building, a distance from another building or
municipal feature (e.g., a school, hospital, fire department,
police department, golf course, gym, airport, main arterial,
highway, freeway, park, pool, vendor or specific type of business
(e.g., delivery firm), building codes (and/or compliance, or lack
thereof, with building codes), building permits, remodeling
permits, and/or information retrieved from an archive or other
database, such as facebook posts, news articles, public records
(e.g., city, county, and/or state records), google maps lookup
tables (e.g., that provide GPS coordinates for addresses), etc. In
some examples, one, multiple, and/or each of the columns 210
comprising a property metric may correspond to another column
including a time-stamp. For instance, the corresponding time-stamp
column may indicate a time the property metric was received,
measured, verified, etc. Although the spreadsheet 206 is discussed,
additionally or alternatively, the property information could be
stored as a comma delimited list, a NoSQL data structure, or any
other data type, data structure, and/or data system.
[0038] In some embodiments, the property metrics corresponding to
each column may be collected from one or more sources, such as a
multiple listing service, a drone, and/or a network application
(e.g., internet application). In some embodiments, the property
metric may comprise a number of bathrooms, a number of bedrooms, a
size of one or more bedrooms, a size of one or more bathrooms, a
ceiling height, a floor plan area (e.g., square feet, square
meters), year of being built, a type of floor plan (e.g., "open"
floor plan, "closed" floor plan, etc.) year of
renovations/remodeling, type of use (e.g., rental, owner's
residence, retail, storage, mixed use, etc.), whether or not the
property is currently inhabited, last date of inhabitance, seller
distress (e.g., any indication of a seller's need to sell quickly),
proximity to one or more building or municipal feature, geographic
region, traffic conditions of roads adjacent and/or near the
property, presence of lead, asbestos, caustic chemicals, mold,
and/or other detectable substances that may negatively impact an
inhabitant's health, wear condition, historical listed prices,
historical transaction information, photos, videos, information
derived from photos and/or videos, data about past, current and/or
future resident, owner, and/or renter (e.g., credit score, marital
status, recent family death, etc.) and/or a current listed price.
In some embodiments, the property metric may comprise a feature or
characteristic with any real numerical value (e.g., 65, 10,000,
0.34, -10), any integer value (1, 5, 65), a binary value (e.g.,
Yes/No, I/O), a quality rating which, in some examples, may be
converted to a numerical value system (e.g., low=1, medium low=2, .
. . high=5, etc.), and/or a predetermined number of options (e.g.,
residential, commercial, mixed residential/commercial and/or
combinations thereof) which, in some instances, may correspond to a
numerical value according to a conversion key.
[0039] In some instances, the property metric may comprise a
derivation from one or more other property metrics. For instance, a
property metric of "renovation costs," "repair costs," and/or
"value maximization costs," may be derived from one or more other
property metrics, such as "square feet" and/or "level of wear". In
some instances, other calculations/determinations may be derived
from the property information database 202. For example, a
potential rehab cost may be calculated/derived from one or more
other calculated/derived values. For instance, a predicted total
incurred cost (e.g., a listed price plus a rehab/renovation) may be
subtracted from a predicted potential value to calculate a
predicted profit. For instance, an "inventory of materials for
renovations" may be generated from one or more of the property
metrics. For instance, a "predicted offer value" may be derived
from a predicted value, as well as other market forces, such as
volume, level of inventory, bullishness, etc.
[0040] In some embodiments, the system 200 may comprise the
property valuation module 204, which may use a linear regression
analysis to calculate a predictive value of the property. The
linear regression calculation may comprise a sum of one or more
input variable values ("the input variable"), represented in FIG. 2
as x.sub.ip. Each input variable value may multiply to a parameter
vector coefficient, represented in FIG. 2 as .beta..sub.p. In some
examples, the input variable value may correspond to one of the
property metrics. Each of one or more parameter vector coefficients
may correspond to data, such as historical data of property
valuations, used to calculate an amount each unit of the input
variable affects the total predicted value. In some instances, the
parameter vector coefficient may be calculated using a maximum
likelihood estimator (MLE) and/or an ordinary least squares (OLS)
model (e.g., to detect fraudulent activity and/or to identify
relevant, but undocumented property information). The parameter
vector coefficient may be calculated using data from other modules
and/or databases of the system. In some examples, the parameter
vector coefficient may be associated with a timestamp that
indicates a time the parameter vector coefficient and/or the
property metric value is received, measured, collected, and/or
calculated. Additionally or alternatively, the property valuation
module 204 may use another statistical model or combinations of
statistical models, such as non-parametric models, semi-parametric
models, Gaussian distributions, chi-squared distributions, nested
models, and/or other regression or classification models.
[0041] In some embodiments, the parameter vector coefficient may be
stored in the memory, as a spreadsheet 224, and/or as a list, as
discussed above with regard to the property metrics. The parameter
vector coefficient may be updated and/or changed based, at least in
part, on an updated, changed, and/or added property metric value of
the property metric to which the parameter vector coefficient
corresponds. For instance, a first parameter vector coefficient may
be calculated based, at least in part on a first property metric
value or multiple property metric values (e.g., a subset or all of
the property metric values stored in the memory), one, multiple,
and/or all of which may be associated with a first timestamp. The
first parameter vector coefficient may be associated with a first
time indicated by first timestamp. A second parameter vector
coefficient that may correspond to a same property metric as the
first parameter vector coefficient may be calculated based, at
least in part, on a second property metric value or a second subset
of property metric values, which may be associated with a second
timestamp. In some examples, the second timestamp may indicate a
second time that is later than the first time of the first
timestamp. In some examples, the second parameter vector
coefficient may replace the first parameter coefficient in the
property valuation module calculations of predicted property
values, based at least in part on a sequence of the first and
second timestamps. In other words, after receiving a new property
metric value (i.e., with a timestamp later than any timestamp of
the previous property metric values), the property valuation module
204 may update a corresponding parameter vector coefficient (e.g.,
automatically, in real-time, and/or without human interaction).
Additionally or alternatively, the property valuation module 204
and/or the real estate offer generation module may be updatable
(e.g., automatically, in real-time, and/or without human
interaction) in a similar manner using one or more timestamps.
[0042] In some examples, the property valuation module 204 may
calculate a predicted property value when a new property is added
to the property information database 202 (e.g., as determined by a
timestamp indicating a time that is later than the timestamp of the
properties previously stored in the property information database
202), each time a property is added to the property information
database 202, when a predetermined property metric value is
received, measured, and/or calculated, and/or when a predetermined
set of property metric values are received, measured and/or
calculated. In some examples, a predicted property value may be
calculated for the property on a schedule of regular or irregular
intervals and one or more calculated predicted values may be stored
in the property information database 202, for instance, in a
"predicted value" column. In some examples, property valuation
module may calculate predicted values for a future time, such as
for investment purposes. For instance, the property valuation
module may calculate a predicted value for about 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
and/or 40 years into the future, and/or a range of years into the
future comprising any two of the aforementioned values.
[0043] In some examples, the property valuation module 204 may
calculate a predicted property value using one or more property
metric values associated with the physical properties and/or one or
more "potential" (e.g., hypothetical) property metric values as
well as financial metrics such as an interest rate, an exchange
rate, and/or a bond yield curve. For instance, the property
valuation module 204 may calculate a "potential" predicted property
value indicating a hypothetical value of the property if certain
criteria were met, the certain criteria comprising one or more of
the "potential" property metric values used to calculate the
"potential" predicted property value. By way of example, the
property valuation module 204 may calculate that the property may
have a "potential" predicted property value if, for instance, the
property metric of "bathrooms" were changed from a property metric
value of "2" to a "potential" property metric value of "3".
[0044] In some embodiments, the property valuation module 204 may
access other databases to calculate the predicted property value.
For instance, the property valuation module 204 may access a
database storing information related to market interest rates such
as the prime rate or the London Interbank Offered Rate (LIBOR),
currency exchange rates such as the exchange rate between Chinese
Renminbi to US Dollars, the bond rate yield curve, or any other
financial rate. In some instances, the property valuation module
204 may access financial information that is specific to a time at
which the financial information is accessed (e.g., is
"up-to-date").
[0045] In some embodiments, the property valuation module 204 may
calculate a predicted property value of the physical property by
calculating a cost to replace and/or build the physical property.
For instance, using one or more property metric values (e.g.,
location, bedrooms, bathrooms, floor plan square footage, garage
square footage, etc.) of the physical property, the property
valuation module 204 may gather quotes for costs of material and
labor to replace the physical property (e.g., by accessing and/or
"scraping" one or more databases through a network, through an MLS,
etc.). In some instances, the property valuation module 204 may
generate a cost to build the physical property (e.g., using one or
more APIs of a construction company and/or hardware store). In some
instances, the property valuation module 204 may identify one or
more comparable properties (e.g., properties that share one or more
similar property metric values, such as square footage, number of
bathrooms and bedrooms, geographic location, etc., with the
physical property) which have been recently built (e.g., within the
last 1, 2, 3, 4, or 5 years), and may adjust a listed price of the
comparable property/ies for other factors (e.g., property metric
values) that are different than the physical property, such as land
value, wear, age, etc. In some instances, this method may be called
the "Cost Approach."
[0046] In some examples, the property valuation module 204 may make
a comparison between prior selling prices or listed prices to a
current or past selling or listed price of the physical property or
a comparable property, which may indicate a condition of the
physical property. For instance, a condition of the physical
property may be determined to be "like-new" if the physical
property sells at a price that is close to (e.g., within 1%, 2%,
3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15% and/or a
range comprising any two of the aforementioned percentages) a
selling price and/or listing price of one or more newly built
comparable property/ies. In some instance, this method may be
called the "Estimated Condition Approach."
[0047] In some embodiments, the property valuation module 204 may
calculate a predicted value of the physical property by comparing
recent sales. For instance, the property valuation module 204 may
identify one or more comparable properties and may quantify a
difference between the physical property and the comparable
property/ies in a dollar amount. For instance, a comparable
property that has some similar property metrics (e.g., floor plan,
year built, etc.) as the physical property may include a listed or
sold price. If the comparable property has some different property
metric values, such as an additional bedroom than the physical
property (e.g., 4 bedrooms instead of 3 bedrooms), and bedrooms are
determined to affect the value by $5,000 in that particular
geographic area, then the physical property may be valued at the
sold price of the comparable property minus $5,000. The comparable
property/ies may be identified using a cluster algorithm, such as
Nearest Neighbor Search (NNS). In some instances, the predicted
value of the physical property may be displayed alongside the
comparable property and its corresponding sold price. In some
examples, this method may be called the "Sales Comparison
Approach."
[0048] In some examples, the property valuation module 204 may
calculate a predicted value of the physical property by calculating
a potential cash flow from rents. For instance, the property
valuation module 204 may identify local rental rates (e.g., using
one or more MLS databases, website databases, etc.) and determine a
comparable rental rate of the physical property (e.g., based, at
least in part, on a number of tenants/potential tenants of the
physical property). The rents may be incorporated into one or more
capitalization rate (cap rate) formulas to determine the predicted
value which may be used to derive what an investor would be willing
to pay. In some instances, a demographic makeup of the geographic
area of the physical property and/or other factors that may affect
a risk profile of likely renters and/or a likely renter turnover
rate may be used to calculate the predicted value. In some
instances, this method may be called the "Income Capitalization
Approach."
[0049] In some embodiments, the property valuation module may use
one, some, or all of the aforementioned methods (The Cost Approach,
The Estimated Condition Approach, The Sales Comparison Approach,
and/or the Income Capitalization Approach") to calculate the
predicted value of the physical property. In some instances, one or
more of the aforementioned methods may be performed automatically
when a property is added to the property information database 202.
In some examples, one or more of the aforementioned methods may be
weighted and/or aggregated to calculate the predicted value (e.g.,
based, at least in part, on a preference of a profile). In some
examples, one or more of the aforementioned methods may be chosen
to calculate the predicted value based on a scenario. For instance,
the Cost Approach may be used to identify a forming property
bubble, and/or to identify cheap underdeveloped land. The Income
Approach may be used to inform a decision to rent or flip a
property, and/or to determine potential economic profits.
[0050] FIG. 3 illustrates an example environment or system 300
configured to collect, store, and/or analyze information from one
or more inputs. In some instances, the system 300 may comprise a
portion of the system/s described above. The system 300 may
comprise a profile preference database 302 which may receive and/or
store information related to one or more entities ("the entity").
As noted above, the entity may comprise an individual, a business,
and/or any other group or organization that may be interested in
purchasing and/or selling real estate property.
[0051] In some examples, the profile preference database 302 may
store one or more profiles ("the profile 304") associated with the
entity. The profile 304 (which may comprise a row of a spreadsheet)
may include information about the entity, such as a name of the
entity, a location of the entity, and/or a type of the entity
(e.g., type of business, status as an individual, etc.). In some
embodiments, the profile 304 may include one or more preferences or
thresholds 306, which may relate to the physical property,
associated with the entity. For instance, the profile 304 may
include a preference for a specific value, a range of values,
and/or a threshold value of one or more property metrics. By way of
example, the profile 304 may include a preference 308 for
"Residential" or "Commercial" property. The profile 304 may
comprise a preference for a floor plan of "greater than 4,500
square feet" or "between 3,500 and 4,500 square feet". The profile
304 may include a preference and/or threshold for any of the
property metrics stored in a property information database 310,
and/or other property metrics. The profile 304 may include a
preference for a type of use (e.g., rental, owner's residence,
retail, storage, mixed use, etc.). The profile 304 may include
preference with a first threshold that is dependent on a second
threshold. By way of example, the profile 304 may include a
preference that "garage square feet>floor plan square feet".
[0052] In some examples, the profile preference database 302 may
comprise the preference and/or threshold value (e.g., associated
with one or more of the profiles 304) for a value calculated from
the property metric values. For instance, the profile 304 may
comprise a threshold and/or preference for a difference between a
"listed property value" property metric value and a "predicted
property value" property metric value. In some instances, the
profile 304 may comprise a threshold and/or preference for a
property metric value that indicates a potential to increase a
value of the property, such as a "high wear" property metric value.
In some instances, the property metric value may comprise
information about one or more structural components (e.g.,
construction materials) constituting the physical property (e.g., a
condition, a dimension, a quantity, a brand, a type, etc.).
[0053] In some embodiments, a plurality of profiles 304 may be
stored in the profile preference database 302. Each of the
plurality of profiles 304 may comprise the value and/or preference
that is unique to the entity associated with each profile 304. In
some embodiments, more than one profile may be associated with the
entity. For instance, a private equity firm may comprise a
different profile for each of multiple investment funds (e.g.,
three different investment funds). The threshold value and/or
preference may be added to the profile preference database 304 at a
time of creating the profile 304, and/or at a time after creating
the profile 304 (e.g., the profile 304 may be updatable). In some
instances, the entity may access information stored in one or more
databases of the system by accessing the profile associated with
the entity (e.g., through a graphical user interface). For
instance, the profile may include automatically generated
descriptions of one or more property/ies, hyperlinks to websites
that mention the property/ies, and/or hyperlinks to files or
documents relevant to the property/ies.
[0054] In some examples, the profile preference database 302 may
comprise a high-interest property list 312. The high-interest
property list 312 may include a list of property identifications
associated with one or more physical properties that have been
determined to be of "high-interest" to the entity associated with
the profile 304 of the high-interest property list 312. In some
embodiments, a property may be determined to be a "high-interest"
property and may be added to the high-interest property list 312 by
the entity (e.g., if the entity is aware of a property that the
entity would like to monitor and/or receive information about). In
some examples, a property may be determined to be the
"high-interest" property by a threshold comparison module 314. For
instance, the threshold comparison module may receive the property
metric value of the property from the property information database
310. The threshold comparison module 314 may receive the threshold
value and/or preference from the profile preference database 302,
and may compare the threshold value and/or preference to the
property metric value/s to determine if the property metric value
meets and/or exceeds the threshold value and/or preference.
[0055] By way of example and not limitation, an example profile 304
may comprise a threshold value of "greater than 3,500" to
correspond with a property metric of "square feet". An example
physical property may have a property metric value of "2,000" for
the "square feet" property metric. In this example, the threshold
comparison module 314 may determine that the property metric value
for the "square feet" property metric of the example physical
property does not meet the threshold value for the example profile
304. In some instances, the determination of whether or not the
property metric value meets and/or exceeds the threshold value may
be stored in the memory (e.g., in the profile preference database
302). In some examples, the property may be added to the
"high-interest" property list, at least in part, because the
property metric value meets and/or exceeds the threshold value.
Additionally or alternatively, the threshold comparison module 314
may compare the property metric value to the threshold value for a
property already included in the "high-interest" property value
list 312. In some examples, the threshold comparison module 314 may
access a profile stored in the profile preference database 302 and
compare every threshold value associated with the profile 304 to a
corresponding property metric value of one or more of the
properties.
[0056] The threshold comparison module 314 may store the comparison
in the profile preference database 302 and/or in the property
information database 310. In some examples, the profile 304 may
provide a preference indicating a threshold value and/or a property
metric that the entity associated with the profile values more
(e.g., deems more important for a buying decision) than other
threshold values and/or other property metrics. In some examples,
the entity may rank one or more threshold values to indicate an
importance of the one or more threshold values to the entity.
[0057] In some examples, the system 300 may comprise a real estate
offer generation module 316 (for instance, the threshold comparison
module 314 may comprise a portion of the real estate offer
generation module 316). The real estate offer generation module 316
may receive information from the property information database 310,
the profile preference database 302, the threshold comparison
module 314, a property valuation module 318, and/or from other
sources. The real estate offer generation module 316 may determine
to generate a real estate offer, generate the real estate offer,
and/or send the real estate offer to an entity associated with the
profile 304 (e.g., to a device 320 of the entity). In some
instances, the real estate offer generation module 316 may generate
a construction supplies list and/or may send the construction
supplies list to an entity (e.g., a home improvement retail store).
In some examples, the real estate offer generation module may send
an instruction to contractor, sub-contractor or other personnel
(e.g., an instruction to an inspector to visit/asses the property,
an instruction to a painter to paint a portion of the property,
etc.)
[0058] In some embodiments, the real estate offer generation module
316 may determine to send the real estate offer based on
information from the property information database 310, the profile
preference database 302, the threshold comparison module 314,
and/or the property valuation module 318. In some instances, the
real estate offer generation module 316 may weigh one or more input
factors to make the determination to send based, at least in part
on a logistic regression analysis. For instance, the profile 304
may include a preference that the entity receive the real estate
offer for a property that meets and/or exceeds a threshold value
and/or multiple threshold values (e.g., as determined by the
threshold comparison module 314, and/or a property listed in the
high-interest property list 312). The profile 304 may include a
multi-property preference, such as a preference to receive a real
estate offer for any property that meets a particular combination
of threshold values and/or preferences. Additionally or
alternatively, the profile 304 may include a property-specific
preference, such as a preference to generate an offer for a
particular property if a combination or one of multiple
combinations of threshold values is met or exceeded. In some
instances one or more properties may be grouped (e.g., based on a
proximity to each other and/or based on a sharing of similar
characteristics) and treated as a single property (e.g., which may
be similar to a "bundle" of stock options). In some examples, the
high-interest property list 312 may store a list of properties for
which the real estate offer generation module 316 may generate an
offer. In some instances, the real estate offer generation module
316 may generate an offer or an alert to sell a property if a
property metric value falls below the threshold 306, according to a
preference of the profile 304.
[0059] In some embodiments, the real estate offer generation module
316 may generate the real estate offer. The real estate generation
module 316 may comprise an application programming interface ("the
API 322") to populate one or more real estate offer forms, such as
a Purchase and Sale Agreement and/or corresponding Addendum. For
instance, after determining to generate the real estate offer, the
API 322 may access information stored in the property information
database 310 and/or the profile preference database 302. The API
322 may identify a field of the real estate offer form that
corresponds with a field of the property information database 310
and/or the profile preference database 302, and the API 322 may
copy data in the field of the property information database 310
and/or the profile preference database 302 into the corresponding
field of real estate offer form. By way of example, the API 322 may
access and copy an "entity name" from the profile preference
database 302, a "property identification" from the property
information database 310, a "predicted offer value" from the
property information database 310 (which may correspond with an
"offer price" field of the real estate offer form), and/or any
other information from the property information database 310 and/or
the profile preference database 302 that may correspond to a field
of the real estate offer form. Additionally or alternatively, the
API 322 may generate a description of the property and/or an
explanation of an offer price based, at least in part, on
information from the property information database 310 and/or the
profile preference database 302. Based, at least in part, on the
determining to generate the offer, the API 322 may generate a same
real estate offer for multiple entities. Additionally or
alternatively, the API 322 may generate a partially completed real
estate offer (e.g., a real estate offer with a majority of the
fields populated, such as about 70%, 80%, 90%, 95%, or 99% of the
fields populated, and/or the fields that are not entity-specific)
and may populate one or more remaining fields with information
unique to the entity to which the partially completed real estate
offer is to be sent at a time after generating the partially
completed real estate offer. In some instance, the API 322 may
recognize offer form parameters that are specific to the entity
(e.g., Veteran's Administration may have a specific purchase and
sale agreement that is required instead of a generic form.)
[0060] In some examples, the real estate offer generation module
316 may send the real estate offer to the entity associated with
the profile 304. For instance, the real estate offer generation
module 316 may generate an email, formatted API message, SMS,
and/or other digital message including the information of the real
estate offer and/or with the real estate offer attached as a
separate file (e.g., word file, PDF file, etc.). The real estate
offer generation module 316 may provide instructions to deliver a
physical copy of the real estate offer, such as through traditional
mail (i.e., "snail mail"). The real estate offer generation module
316 may send the real estate offer to the computing device 320 of
the entity. In some examples, the real estate offer may be sent to
the entity automatically (e.g., autonomously, without human
interaction, etc.) and/or the real estate offer may be sent to an
intermediary reviewer. In some examples, the offer may comprise an
offer/request to visit the property in addition to or alternatively
to an offer to purchase the property. In some instances, the entity
may indicate a desire to visit one or more properties based on
information provided by the real estate offer generation module
316. The entity may access/retrieve an offer that is stored in one
or more databases of the system 300 in addition to or in lieu of
the real estate offer generation module 316 sending the offer.
[0061] In some examples, the real estate offer generation module
316 may determine to generate a real estate offer, generate a real
estate offer, and/or send a real estate offer automatically, in
real-time, and/or without human interaction. The real estate offer
generation module 316 may periodically assess the property
information database 310 and/or the profile preference database 302
to determine if information has been added (e.g., "new"
information) since a last time the real estate offer generation
module 316 assessed the property information database 310 and/or
profile preference database 302. For instance, the real estate
offer generation module 316 may assess the property information
database 310 and/or the profile preference database 302 every
microsecond, millisecond, second, 10 seconds, minute, 10 minutes,
hour, 4 hours, and/or day. If the real estate offer generation
module 316 determines that new information has been added to the
property information database 310 and/or the profile preference
database 302 since the last assessment, the real estate offer
generation module 316 may determine to generate a real estate
offer. In some examples, the real estate offer generation module
316 may determine whether or not to generate a real estate offer
each time information is added to the property information database
310 and/or the profile preference database 302. The determination
to generate the real estate offer may be based, at least in part,
on a preference of the profile 304 and/or the high-interest
property list 312 and/or may use a logistic regression
analysis.
[0062] In some embodiments, other modules may generate populated or
at least partially populated documents, forms, and/or messages. For
instance, a title report module may generate a preliminary title
report for a title company (e.g., may generate an email, may
populate a document using an API, etc.) and/or send a request to a
third party to generate a title report. A scheduling module may
generate a schedule for a visit and/or a request for a visit via an
email, text message, and/or calendar invite (e.g., a walkthrough
with a contractor and/or a potential buyer, etc.). An inspection
module may generate a request for an inspection from an inspection
company. An appraisal module may generate a request for an
appraisal which may be sent to an appraisal company. A loan
underwriting module may generate a request to begin a loan
underwriting process to a hard money lender and/or a mortgage
company and/or the loan underwriting module may generate a
populated mortgage document. A finance module may generate budgets
(e.g., rehab budgets, total selling costs budget, likely tax
liability budget, etc.), loan interest report, amortization table,
and/or other financial documents associated with a physical
property. Any of the aforementioned modules may access one or more
of the databases and/or other modules of the system 300 in a
similar or different manner as the real-estate offer generation
module 316, and may use any of the aforementioned communication
methods for sending a generated document, form, and/or message.
[0063] In some instances, data stored in the profile preference
database 302 and/or the property information database 310 may be
stored securely with data encryption. In some instances, at least
some of the data may be stored for a predetermined time period and
then may be deleted after the predetermined time period has
elapsed. In some examples, some or all of the data may be
transferred (e.g., sold) to a third party. The profile preference
database 302 may comprise an option so that the entity associated
with the profile may indicate a preference for what is done with
the data prior to and/or after the data is used by one or more
modules of the system 300. The profile preference database 302 may
comprise one or more options (e.g., check-boxes) to turn on/off
and/or activate any of the modules of the system 300 and/or any of
the functionalities of the modules of the system 300. In some
embodiments, the data may be surfaced to the public through a web
portal or mobile app. Access to the date may be provided in
conjunction with advertising. In some instances, access to the data
may be restricted to paying subscribers. In some instances, limited
portions of the data may be provided freely while other portions
may be provided to paying customers. Access to the data may be
limited by geographic region, type of client used to access the
data, government license or certification held by the user, and/or
any other characteristic of the user or the user's condition.
[0064] FIG. 4 illustrates an example environment or system 400
configured to collect, store, and/or analyze information from one
or more inputs. In some instances, the system 400 may comprise a
portion of the system/s described above. The system 400 may
comprise a property information database 402 which may receive data
from one or more sources, such as a multiple listing service (MLS),
a drone 404, and/or a network 406. In some instances, a data
supplementation module 408 may provide information to the property
information database 402 from one or more sources.
[0065] In some examples, the system 400 may comprise the data
supplementation module 408 which may communicate with and/or access
the property information database 402 and/or a profile preference
database 410. The data supplementation module 408 may access
information provided from a first source (e.g., the MLS) to
identify missing information 412 (e.g., a property metric field
missing a property metric value) and/or unverified information. The
data supplementation module 408 may send a request for and/or
retrieve the missing data 412 and/or unverified information from a
second source (e.g., the drone 404, the network 406, an Amazon
Mechanical Turk, an automated telephone polling service, an
internet survey, and/or another MLS 410), or from the first
source.
[0066] For instance, the property information database 402 may
receive a first collection of information (one or more property
metric values) from a first source, such as the MLS. The first
collection of information may comprise one or more property
identifications associated with one or more physical properties
416, and/or one or more property metric values associated with each
of the property identifications. The data supplementation module
408 may identify a property metric field that is missing data 412,
and/or the data supplementation module may determine that the
missing data 412 is "relevant" (e.g., worth using resources to
retrieve). For instance, the data supplementation module 408 may
determine that the missing data 412 is "relevant" because the
missing data 412 corresponds to a property metric (e.g., is in a
property metric column) that is predetermined to be a
high-relevance property metric. In other words, one or more
property metric columns may be designated as a "high-relevance"
column (e.g., by the entity, by the profile preference database
410, by the value prediction module, etc.), such that the data
supplementation module 408 recognizes any missing data in the
"high-relevance" column as "relevant" information, and proceeds to
retrieve the missing data.
[0067] In some embodiments, the data supplementation module 408 may
identify missing information 412 that is "relevant" and/or may
retrieve the missing information 412 based, at least in part, on a
preference stored in the profile preference database 410. For
instance, a profile may indicate one or more property metrics that
are "relevant" to the entity associated with the profile. The data
supplementation module 408 may cross-reference property metrics
determined to be "relevant" to an entity in the profile preference
database with property metric values missing in the property
information database 402 to identify missing property metric values
that are "relevant". Additionally or alternatively, the data
supplementation module 408 may identify a property metric value
that is not missing (e.g., provided in the first collection of
information), but determined to be "relevant". Such information may
be determined to be "relevant" and unverified (e.g., from a single
source, from a questionable source, etc.) and the entity may
provide an indication that the entity would like the "relevant"
information to be verified. In some instances, the "relevant" data
may be determined to be unverified because the data was received
more than a certain amount of time ago, such as a month ago, two
months ago, three months ago, six months ago, etc. The certain
amount of time may comprise a preference of the profile preference
database 410.
[0068] In some examples, the data supplementation module 408 may
identify missing information 412 that is "relevant" and/or may
retrieve the missing information 412 based, at least in part, on
the property valuation module. For instance, the property valuation
module may determine that a property metric may be useful for
generating a predicted property value (e.g., that including the
property metric value in the predicted value calculation may reduce
an error, reduce an error range, and/or increase an accuracy of the
prediction). The data supplementation module 408 may
cross-reference the property metric determined to be useful by the
property valuation module with the property metric value in the
property information database 402 to identify "relevant" property
information, such as "relevant" property information that is
missing and/or unverified.
[0069] In some examples, the data supplementation module 408 may
send a request to receive information that supplements and/or
verifies the identified "relevant" missing and/or unverified
information. In some embodiments, the data supplementation module
408 may send a request to the drone 404 to provide the missing
and/or unverified information. For instance, the request may
include location information of the physical property 416 of the
missing information (e.g., from the property information database
402) and/or a property metric to be measured, recorded, identified
at the physical property 416 and/or at a neighborhood 418 of the
physical property 416. In some examples, the data supplementation
module 408 may receive a confirmation (e.g., a message and/or
signal) that the request for information has been received by the
drone 404.
[0070] In some embodiments, the data supplementation module 408 may
send a request to receive data to supplement and/or verify the
identified "relevant" missing and/or unverified information through
the network 406, such as an internet or intranet. For instance, the
data supplementation module 408 may comprise a web analytic module
to access a website and/or a database. The database may comprise a
database of a web service company, such as a social media site 420
(e.g., Facebook, LinkedIn, Twitter, Instagram, and the like),
another real estate database (e.g., Zillow.RTM., Trulia.RTM.) a
news organization archive 422 (e.g., Reuters, BBC, the New York
Times, and/or a regional news outlet), historical records and/or
public records 424 (e.g., from a city hall) which collects
information about individuals, businesses, organizations, and/or
other entities that may be related to buying or selling physical
property. In some examples, the web analytic module may comprise an
API, such as a RESTless API and/or a RESTfull API, which may send a
request for the missing and/or verifying information to one or more
servers hosting the database. In some embodiments, the web analytic
module may gather (e.g. "scrape") data from a website, which may be
accessed by the data supplementation module 408.
[0071] In some instances, the data supplementation module 408 may
assign one or more data sources a credibility score. The
credibility score may be assigned manually by a user or based on
historical verification events. By way of example, if a square
footage metric received from the Spokane County Tax Assessors
Office is determined to have been repeatedly found to be incorrect
during past verification events, the Spokane County Tax Assessors
Office may receive a low credibility score. By way of example, if a
real estate agent posts information to a website (e.g., facebook)
describing a target property, and the posted information is
verified by one, some, and/or all historical events related to the
target property, future information created by the real estate
agent may be given a high credibility score. In some examples, the
data supplementation module 408 may compare the credibility score
associated with a data source against a credibly threshold score in
order to determine whether a verification of "relevant" information
is required.
[0072] In some examples, the data supplementation module 408 and/or
the property information database 402 may receive the missing
and/or verifying information requested by the data supplementation
module 408. For instance, the source (e.g., the drone 404, the
database accessed through the network 406, etc.) may send the
requested information to the property information database 402
and/or to the data supplementation module 408 (which may send the
requested information to the property information database 402). In
some examples, the data supplementation module 408 may fill the
previously identified missing information 412 field of the property
information database 402 with the received missing information. In
some examples, the property information database 402 may comprise a
column or list indicating a verification status associated with a
property metric (i.e., indicating whether or not a property metric
has been verified from one or multiple sources). The data
supplementation module 408 may change a verification status (e.g.,
from "No" or "0" to "Yes" or "1") based, at least in part on, the
received verification information.
[0073] FIG. 5 illustrates an example environment or system 500
configured to collect, store, and/or analyze information from one
or more inputs. In some instances, the system 500 may comprise a
portion of the system/s described above. The system 500 may
comprise a drone 502 (i.e., an unmanned aerial vehicle) which may
communicatively couple to one or more modules of the system, such
as a data supplementation module. The drone 502 may comprise a
frame for mounting components and/or a housing 504 for enclosing at
least some of the components. The frame and/or housing 504 may
comprise a substantially rigid material, such as metal alloys,
plastic (e.g., PVC, PE, Nylon, etc.), wood, carbon fiber, Kevlar,
combinations thereof, and/or composites thereof. In some
embodiments, the drone 502 may comprise a bladder of fluid having a
density that is lower than air, such as helium and/or hydrogen.
[0074] In some examples, the drone 502 may comprise a power system
506 to provide power to some and/or all of one or more components
(i.e., electrical components) of the drone. The power system 506
may comprise one or more batteries ("the battery"), such as a
lithium ion battery. The battery may be rechargeable, for instance,
when the drone 502 couples to a charging dock (e.g., wired charging
and/or wireless inductive charging). In some examples, the drone
502 may comprise a recharger for the battery, such as a control
system for switching the battery on/off to a charging bus bar
coupled to an alternative power source, such as a solar panel. The
power system 506 may provide power to one, some, any, and/or all of
the components of the drone 502 that use power for operation.
[0075] In some examples, the drone 502 may comprise a
microcontroller 508 which may communicatively couple to one or more
components of the drone 502, e.g., through switches, resistors,
and/or other electronic components. For instance, the
microcontroller 508 may comprise a small computer comprising a
processor 510, a memory 512 (storing one or more executable modules
and/or an operating system 514), and/or one or more programmable
input/output ports 516, which may, in some examples, comprise a
single integrated circuit. In some examples, the microcontroller
508 may communicatively couple to a transmitter, receiver,
transponder, and/or transceiver such as an RF transceiver 518. The
transceiver 518 may send and/or receive data to and/or from
components/modules of the system 500. For instance, the drone 502
may receive and/or send data to another transceiver/receiver of the
data information collection, storage, and/or processing system
(e.g., the data supplementation module, the property valuation
module, etc.).
[0076] In some examples, the drone 502 may comprise one or more
propulsion devices 520, such as one or more propellers ("the
propeller 522") coupled to one or more motors ("the motor 524"),
such as a brushless motor and/or a multi-rotor motor. Additionally
or alternatively, the drone 502 may comprise jet propulsion,
magnetic propulsion, or other types of propulsion devices. In some
examples, the drone 502 may comprise a single propeller 522 or
multiple propellers 522 (e.g., two, three, four, five, six, seven,
eight, nine, ten, or more than ten propellers), each propeller 522
may connect to one of the motor/s 524 that converts power from the
power system 506 into an angular force that rotates the propeller
522. In some embodiments, the propeller 522 may be mounted to an
arm 526 extending horizontally from a body of the drone 502 and/or
may be oriented with a vertical spin axis (such that the propeller
522 may provide a vertical lift of the drone 502). In some
embodiments the motor 524 may couple to a speed controller which
may comprise a portion of the microcontroller 508 and/or
communicatively couple to the microcontroller 508. The drone 502
may comprise a 3-axis gyroscope, accelerometer, magnetometer,
and/or ultrasound sensors to provide information for controlling
the motor 524.
[0077] In some embodiments, the drone 502 may comprise a location
module 528 for determining a location of the drone 502, navigating
the drone 502 to and/or from a location (such as a location of a
physical property) and/or for geocoding the location (e.g., the
drone 502 may comprise spatial awareness software such as
Google.RTM. Tango.RTM.). For instance, the location module 528 may
comprise a geographic information system (GIS) (e.g., for
collecting vector and/or raster data) to capture, store,
manipulate, analyze, and/or manage spatial and/or geographical
information. In some examples, the location module 528 may operate
according to one or more established standards, such as an open
geospatial consortium (OGC) standard (e.g., CSW, GML, etc.). In
some embodiments, the location module 528 may access data of other
location information services, such as Google Maps, Landsat, GPS,
etc. In some embodiments, the drone 502 may comprise a mechanical
and/or digital compass, which may communicatively couple to the
location module 528. The location module 528 may form a portion of
the microcontroller 508 and/or may communicatively couple to the
microcontroller 508.
[0078] In some embodiments, the drone 502 may comprise a flight
controller 530, which may comprise a portion of the microcontroller
508 and/or may communicatively couple to the microcontroller 508.
For instance, the flight controller 530 may communicate with and/or
control the motor 524 to stabilize the drone 502. In some instances
the flight controller 530 may control one or more motors 524, may
change the power provided to the motor 524 over a duration of time,
may control the drone 502 with autopilot, and/or may direct the
drone 502 from a first location to a second location. In some
instances a direction of the flight controller 530 to the motor 524
may correspond to instructions received by the drone 502 including
location information associated with a physical property. In some
examples, the drone 502 may receive a set of location instructions
(e.g., detailing one or multiple legs of a route) and/or the drone
502 may receive a starting location and an ending location and/or
the drone 502 may calculate/determine a flight path.
[0079] In some examples, the drone 502 may receive a flight path
(e.g., through a continuous stream of instructions and/or through a
stored download of instructions) and/or the drone 502 may be
controlled remotely. The drone 502 may receive/calculate an
optimized flight path based on one or more factors such as fuel
usage, temperature, wind, humidity, and/or FAA regulations for
elevations, prohibited areas, and/or prohibited flight times of
day. The drone 502 may be controlled by a remote control and/or
wirelessly through a 2.4 Ghz, 5 Ghz, and/or other frequency
connection. In some examples, the remote control may be handheld,
installed in a vehicle, and/or installed at a drone dispatch
building. The drone 502 may comprise a route which switches between
autopilot and manual control (via the remote control), such as
dispatching and travelling a distance to the physical location on
autopilot, then switching to manual control once the drone 502
reaches the physical location. In some examples, the remote control
may control multiple drones 502 assigned to collect property
information corresponding to multiple property identifications
stored in the property information database. In some examples, the
drone 502 may comprise one or more autopilot platforms to provide
interact/instruct the components of the drone 502, e.g., the motor
524. For instance, the drone 502 may comprise OpenPilot, Paparazzi,
3DR, APM, and/or SmartAP software. In some instances, the flight
controller 530 may be configured to follow a ping (such as a ping
from hardware at the physical property, from a mobile device,
etc.).
[0080] In some examples, multiple drones 502 may idle (e.g.,
charging, stored, docked, having standby status) at a single
location or multiple locations. The idling drones may receive
information corresponding to multiple property identities stored in
the property information database and may be dispatched from the
single or multiple locations. In some embodiments, multiple drones
502 may survey and/or collect information at a single location
and/or may collect data to construct a 3-D image, and/or any other
information that may be re-constructed for presenting a virtual
simulation of the property.
[0081] In some examples the drone 502 may comprise communication
components (i.e., hardware 532 that may correspond with software),
such as speaker/s, display/s (e.g., LCD display/s), camera/s,
and/or microphone/s. For instance, the drone 502 may comprise
components for communicating with people, external devices such as
an automatic garage door opener, and/or other drones in an
immediate environment of the drone 502. The drone 502 may provide
and/or receive instructions to/from people and/or other drones. In
some examples, the drone 502 may comprise human interaction
artificial intelligence software to instruct one or more hardware
components to interact with the immediate environment and/or people
in the immediate environment.
[0082] In some embodiments the drone 502 may comprise one or more
sensors 534 and/or components for collecting information of the
immediate environment, such as the physical property and/or the
neighborhood of the physical property. For instance, the drone 502
may comprise one or more cameras for capturing photos and/or video,
an infrared sensor, x-ray sensor, radiation detector, chemical
analysis sensor, voltmeter/current meter, spectroscopy, wireless
transmission viability sensor, x-ray diffraction/x-ray fluorescence
instrument, a sample acquisition instrument and/or 3-D scanning
cameras and/or software, such as Google's Project Tango or
Matterport. The one or more sensors 534 may detect asbestos, lead,
moisture, material compositions, controlled substances (e.g.,
drugs, meth, heroine, crack), cigarettes, mold, wet paint, cleaning
chemicals, dangerous chemicals, gas (such as a gas leak), metallic
erosion, oxidization, resistance in wiring of the physical
property, and/or an infestation (e.g., rodents, insects, birds,
cats, and/or people, such as squatters).
[0083] By way of example, the drone 502 may capture audio,
photographs, and/or video of the physical property and/or features
of the physical property, which are compared to a pre-stored audio,
photograph, and/or video profile. For instance, the drone may
record an audio of "scurrying sounds" and compare it to a
pre-stored audio profile to identify the "scurrying sounds" as
mice. In some examples, the drone may collect one or more audio
samples of the physical property, which may be used to determine an
exterior and/or interior audio profile of the physical property
(e.g., to indicate noise pollution such as from a nearby highway).
The exterior and/or interior audio profile may be compared to one
or more predetermined audio profiles to identify sound generating
features and/or the exterior and/or interior audio profile may be
accessed and/or listened by an entity, such as a potential buyer.
By way of example, the drone 502 may capture a photograph of the
physical property and compare it to a pre-stored photograph to
determine the presence of, for instance, corrosion, oxidization,
mold, etc. By way of example, the drone 502 may collect a sample
(e.g., with a robotic arm, an adhesive, a collection box, an air
intake, etc.) to assess a chemical composition of the sample.
[0084] In some examples, the drone 502 may comprise a network
connection 536, such as an external memory connection, a USB port,
an Ethernet port, an optical fiber port, and/or the like. For
instance, the drone 502 may be configured to upload property
information that has been collected and/or stored through the
network connection 536. The drone 502 may travel to an uploading
location (e.g., a vehicle, a dispatch center building, a data
collection center building, etc.) after collecting the property
information. The drone 502 may connect to a computing device
through the network connection 536 to upload the property
information system into the system 500 (e.g., to the property
information database). Additionally or alternatively, the drone 502
may upload property information wirelessly, e.g., using the
transmitter, transceiver 518 and/or transponder, through a data
connection (e.g., 4G, 5G, LTE network, etc.), through Wi-Fi, and/or
the like. The drone 502 may upload individual data packets
corresponding to each piece of information collected by the drone
502 in a real-time manner, e.g., at a time substantially
immediately following the time at which the piece of information is
collected (e.g., a millisecond, a second, a minute and/or an hour
after the information is collect) and/or the drone 502 may compile
multiple pieces of information into a single data package and/or
report for uploading after some time (e.g., a minute, an hour, a
day, multiple days) has elapsed from the time of collecting the
information and/or according to an upload schedule.
[0085] FIG. 6 illustrates an example environment or system 600
configured to collect, store, and/or analyze information from one
or more inputs. In some instances, the system 600 may comprise a
portion of the system/s described above. FIG. 6 also illustrates an
example drone 602 of the system 600 which may collect, store,
and/or upload information of a physical property 604, such as
information which may affect a value of the physical property
604.
[0086] As noted above, the drone 602 may receive location
information of the physical property 604 and/or a property metric
of the physical property to be measured, identified, collected,
and/or calculated by the drone. Additionally or alternatively, the
drone 602 may receive instructions to determine one or more
property metric/s to measure, identify, collect, and/or calculate
once the drone 602 has arrived at the physical property 604. The
drone 602 may conduct an initial survey of the physical property
604 to determine which property metric of a predetermined list of
property metrics is applicable to the physical 604 property and/or
worth using resources to acquire. In some instances, the drone 602
may be dispatched in response to receiving an instruction from a
module of the system 600 such that the drone 602 may be dispatched
automatically, autonomously, and/or without human interaction.
[0087] In some examples, the drone 602 may use the location
information of the physical property to travel (e.g., fly) to the
physical property 604. The drone may direct one or more sensors 606
at the physical property (e.g., a feature of the physical property
608) to acquire a property metric value corresponding to the
property metric to be measured, as noted above with regard to FIG.
5. The drone 602 may collect information indicating a status of one
or more features of the house (e.g., a window, roof, foundation,
yard, patio, sprinkler system, etc.). In some examples, the drone
602 may comprise a camera to provide a real-time virtual reality
experience of the physical property (e.g., similar to Google Street
View). The drone 602 may comprise 3-D mapping software, such as
Matterport and/or Google.RTM. Tango.RTM.. The drone 602 may collect
and send video, which may be in real-time, to a display (e.g.,
computer screen, mobile device screen, wearable devices such as a
Google.RTM. Glass.RTM. headset, immersion devices such as an Oculus
Rift.RTM. headset, etc.) of an exterior and/or interior of the
physical property 604. In some instances, the drone 602 may be
controllable by a viewer of the real-time display to provide
interactions with the drone and a virtual 3-D environment. In some
examples, information collected by the drone 602 may be sent to an
implantable computing device which may, in some examples, send the
information to the display.
[0088] In some examples, the drone 602 may assess the property 604
to collect a predictive view with the camera. For instance, the
drone 602 may capture a first image from a first location, which
may include an obstruction (e.g., trees). The drone 602 may move to
a second location to capture a second image, which may illustrate a
view omitting the obstruction (e.g., above or around the
obstruction), and which may be more desirable to a potential buyer,
e.g., may include a vista, natural feature (e.g., Mt. Rainier),
cityscape, landscape, etc., than the view from image of the first
location including the obstruction. In some examples, the drone 602
may collect information documenting potential improvements (e.g.,
cutting down trees and/or removing obstructions) which may increase
a value of the physical property 604.
[0089] In some examples, the drone 602 may be designated to assess
the property prior to receiving the instruction and/or location
information. Additionally or alternatively, the drone 602 may
comprise one of multiple drones of a standby drone swarm. For
instance, the instruction may be sent to multiple drones having a
standby status and may include a request for any available drone
(e.g., within a proximity to the system 600 and/or the property
604) to indicate its availability so that the instruction may be
assigned to the responding standby drone. In some instances, a
human may be sent to the physical property 604 in addition to or
alternatively to the drone 602. The human may collect, measure,
store, and/or send any of the data discussed above with respect to
the drone, such as with a tablet computing device and one or more
sensors (e.g., a voltmeter, a camera, etc.).
[0090] FIG. 7 illustrates an example environment or system 700
configured to collect, store, and/or analyze information from one
or more inputs. In some instances, the system 700 may comprise a
portion of the system described above. In some examples, the system
700 may comprise a physical property 702, which may include an
access point 704. The access point 704 may comprise a barrier
and/or transition from an exterior of the physical property 702 to
an interior of the physical property 702, from a first exterior
space to a second exterior space of the physical property 702,
and/or from a first interior space to a second interior space of
the physical property 702.
[0091] In some examples, the system 700 may include a security door
706, such as an electronically and/or mechanically controlled panel
and/or barrier installed in a door, window, and/or other access
point 704 of the physical property 702. For instance, the security
door 706 may comprise a hinged or rollable door connected to an
actuator which may cause the door to open (e.g., roll up, swing up,
etc.) and/or close (e.g., roll down, swing down) upon actuation.
For instance, the security door 706 may comprise one or more
springs, motorized rollers, and/or hydraulic features
communicatively coupled to a locking feature 708. The locking
feature 708 may comprise a sensor 710 and/or mechanical actuator to
interact with a security access feature of the drone. For instance,
the locking feature 708 may comprise communication equipment using
radio-frequencies, Near-Field Communication, Bluetooth, Wi-Fi,
other electro-magnetic frequencies (e.g., infrared, ultraviolet,
x-ray etc.), magnetic readers, and/or any other type of wireless
platform, which may communicate with corresponding hardware and/or
software of a drone 712. In some instances, the security door 706
may be elevated from a floor or ground of the physical property 702
such that it provides access to aerial devices, such as the drone
712 while preventing access to animals.
[0092] In some examples, the drone 712 may receive an access code
714 from a module 716 of the system 700, such as a data
supplementation module, to provide access for the drone 712 to the
physical property 702 (e.g., the interior of the physical property
702). The access code 714 may comprise a key (which may be
encrypted) that corresponds to the locking feature 708 of the
security door 706 so that, when the drone 712 is in proximity to
the locking feature 708 and/or when the drone 712 transmits 718 the
access code 714 to the locking feature 708, the actuator triggers
an opening and/or closing of the security door 706. In some
examples, the access code 714 may provide a one-time use or the
access code 714 may provide multiple-uses. The access code 714 may
be sent to a single or multiple drones 712. In some embodiments,
the drone 712 may be programmed with the access code 714 as a part
of the installation of the security 706 door at the physical
property 702.
[0093] In some examples, the security door 706 may be installed at
a location that is different than the physical property 702. For
instance, the security door may be installed at a drone dispatch
center, a drone storage facility, a drone storage vehicle, and/or
the like.
[0094] FIG. 8 illustrates an example environment or system 800
configured to collect, store, and/or analyze information from one
or more inputs. In some instances, the system 800 may comprise a
portion of the system/s described above. The system 800 may
comprise one or more geographic regions ("the geographic region
802") In some examples, the geographic region 802 may comprise a
street, a block, a neighborhood of multiple blocks, an area defined
by zoning codes, a municipality, a state, a country, multiple
countries, and/or combinations thereof. The geographic region 802
may comprise one or more physical properties 804 with corresponding
information stored in the property information database. In some
examples, the profile preference database may include a preference
for a physical property 804 within one or more geographic regions
802. In some instances, an entity associated with a profile may
indicate the preference for the geographic region by drawing and/or
outlining boundaries on a displayed map (such as a digital mapping
interface).
[0095] In some embodiments, one or more drones 806 may collect,
store, and/or analyze data about the geographic region 802 which
may affect a value of one or more physical properties 802 within
the geographic region. For instance, using one or more sensors, the
drone 806 may determine characteristics of the geographic region
802, such as a location of a building (e.g., school, hospital, fire
department, business), park, traffic patterns, foot traffic
patterns, and the like.
[0096] In some examples, the drone 806 may set one or more
geolocation markers to record one or more locations of one or more
characteristics/features. The drone may measure a distance 808 from
a first location (e.g., as indicated by a first geolocator 810) to
a second location (as indicated by a second geolocator 812). For
instance, the drone 806 may travel from the first location along a
type of path (e.g., a path following automobile roads, bicycle
trails, walking paths, "as the crow flies", and/or combinations
thereof) to the second location. The drone 806 may measure the
distance 808 between the first location and the second locations,
may associate the distance 808 with the type of path, and/or may
send data comprising the distance 808 to one or more databases
and/or modules 814 of the system 800.
[0097] In some embodiments, the drone 806 may collect information
about the geographic region 802 and/or the one or more physical
properties 804 in the geographic region 802, and the drone 806 may
add the collected information to the property information database
of the system 800. In some examples, the drone 806 may collect
information about the geographic region 802 and/or a physical
property 804 in the geographic region 802 in response to a
preference stored in the profile preference database, in response
to a request from the data supplementation module, and/or in
response to a request from the entity of the system 800.
CONCLUSION
[0098] Although this disclosure uses language specific to
structural features and/or methodological acts, it is to be
understood that the scope of the disclosure is not necessarily
limited to the specific features or acts described. Rather, the
specific features and acts are disclosed as illustrative forms of
implementation.
* * * * *