# US 2021/0279824 A1 · Property Valuation Model and Visualization > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0279824 A1 - Document ID: US20210279824A1 - Status: Published application - Kind: A1 - Application number: 16/807,440 - Filed: 2020-03-03 - Published: 2021-09-09 - Earliest claimed: 2020-03-03 - Assignees: S&P Global Inc. - Applicants: S&P Global - Inventors: Hyunyoung Choi; Hao Yan; Xu Cai; Qiuhao Zhang; Dun Wang; Hao Jiang - Web page: https://patentgrep.com/patent/US20210279824A1 ## CPC - G06Q 50/16: Real estate - G06Q 30/0206: {Price or cost determination based on market factors} - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract Automated property value calculation is provided. The method comprises receiving historic transaction data for a group of real estate properties over a specified time and receiving characteristic data regarding the properties for a number of defined categories. Historic data is also received for a number of demographic parameters over the specified time. The demographic data corresponds to regions in which the properties are located. A predictive valuation model is built with the financial transaction data, characteristic data, and demographic data. Individual values are calculated with the predictive valuation model for a new group of real estate properties according to their characteristics. The individual values are then aggregated. Financial transaction data is received for the new group of properties, and a net asset value of the new group of properties is calculated according to the aggregated valuations and financial transaction data. ## Claims ### Claim 1 A computer-implemented method of automated property value calculation, the method comprising: using one or more processors to perform the steps of: receiving historic financial transaction data for a first number of real estate properties over a specified time frame; receiving characteristic data regarding the first number of real estate properties, wherein the characteristic data comprises a number of defined categories; receiving historic demographic data for a number of demographic parameters over the specified time frame, wherein the demographic data corresponds to defined geographic regions in which the first number of real estate properties are located; build a predictive valuation model based on the financial transaction data, the characteristic data, and the demographic data; calculating individual values for a second number of real estate properties with the predictive valuation model according to characteristics of the second number of real estate properties; aggregating the individual values of the second number of real estate properties; receiving financial transaction data for the second number of real estate properties; and calculating a net asset value of the second number of real estate properties according to the aggregated valuations and financial transaction data. ### Claim 2 (depends on 1) The method of claim 1, wherein training the predictive valuation model comprises: grouping properties according to specified types and characteristics; engineering features; selecting features; training a number of modeling algorithms according to the property grouping, engineered features, and selected features; and validating model performance. ### Claim 3 (depends on 2) The method of claim 2, further comprising: calculating a number of similarity scores between real estate properties according to characteristics; and organizing real estate properties into a number of sets according to their similarity scores. ### Claim 4 (depends on 1) The method of claim 1, further comprising, after training the predictive valuation model: receiving new real estate financial transaction data, new real estate characteristic data, and new demographic data at specified periodic time intervals; and retraining the predictive valuation model based on the new real estate financial transaction data, new real estate characteristic data, and new demographic data for each specified time interval. ### Claim 5 (depends on 1) The method of claim 1, further comprising calculating a bias correction for the calculated net asset value. ### Claim 6 (depends on 1) The method of claim 1, wherein the predictive valuation model comprises decision tree-based algorithms. ### Claim 7 (depends on 1) The method of claim 1, wherein the net asset valuation is calculated according to time series analysis and Kalman filtering. ### Claim 8 (depends on 1) The method of claim 1, wherein receiving the characteristic data further comprises: receiving the characteristic data from a number of different data sources, wherein the different data sources use different classification categories; constructing a multi-layer property linking model that correlates the different data sources; calculating a score for each layer of the multi-layer property linking model for each real estate property; calculating a final score from a weighted average of the scores for the layers of the multi-layer property linking model; and creating a universal property identifier for each real estate property based on the final score. ### Claim 9 (depends on 8) The method of claim 8, wherein the final score determines if records in different data sources refer to the same real estate property. ### Claim 10 (depends on 1) The method of claim 1, wherein receiving the characteristic data further comprises: receiving characteristic data from a number of real estate-specific data sources; receiving tenant data from a number of non-real estate-specific data sources, wherein a subset of the non-real estate-specific data sources comprise tenant occupancy data not included in the real estate-specific data sources; constructing a multi-layer tenant linking model that correlates tenant occupancy data from the real estate-specific data sources and non-real estate-specific data sources; calculating a score for each layer of the multi-layer tenant linking model for each real estate property; calculating a final score from a weighted average of the scores for the layers of the multi-layer tenant linking model; and determining tenant occupancy for each real estate property according to the final score. ### Claim 11 A system for automated property value calculation, the system comprising: a storage device configured to store program instructions; and one or more processors operably connected to the storage device and configured to execute the program instructions to cause the system to: receive historic financial transaction data for a first number of real estate properties over a specified time frame; receive characteristic data regarding the first number of real estate properties, wherein the characteristic data comprises a number of defined categories; receive historic demographic data for a number of demographic parameters over the specified time frame, wherein the demographic data corresponds to defined geographic regions in which the first number of real estate properties are located; build a predictive valuation model based on the financial transaction data, the characteristic data, and the demographic data; calculate individual values for a second number of real estate properties with the predictive valuation model according to characteristics of the second number of real estate properties; aggregate the individual values of the second number of real estate properties; receive financial transaction data for the second number of real estate properties; and calculate a net asset value of the second number of real estate properties according to the aggregated valuations and financial transaction data. ### Claim 12 (depends on 11) The system of claim 11, wherein training the predictive valuation model comprises: grouping properties according to specified types and characteristics; engineering features; selecting features; training a number of modeling algorithms according to the property grouping, engineered features, and selected features; and validating model performance. ### Claim 13 (depends on 12) The system of claim 12, further comprising: calculating a number of similarity scores between real estate properties according to characteristics; and organizing real estate properties into a number of sets according to their similarity scores. ### Claim 14 (depends on 11) The system of claim 11, further comprising, after training the predictive valuation model: receiving new real estate financial transaction data, new real estate characteristic data, and new demographic data at specified periodic time intervals; and retraining the predictive valuation model based on the new real estate financial transaction data, new real estate characteristic data, and new demographic data for each specified time interval. ### Claim 15 (depends on 11) The system of claim 11, further comprising calculating a bias correction for the calculated net asset value. ### Claim 16 (depends on 11) The system of claim 11, wherein the predictive valuation model comprises decision tree-based algorithms. ### Claim 17 (depends on 11) The system of claim 11, wherein the net asset valuation is calculated according to time series analysis and Kalman filtering. ### Claim 18 (depends on 11) The system of claim 11, wherein receiving the characteristic data further comprises: receiving the characteristic data from a number of different data sources, wherein the different data sources use different classification categories; constructing a multi-layer property linking model that correlates the different data sources; calculating a score for each layer of the multi-layer property linking model for each real estate property; calculating a final score from a weighted average of the scores for the layers of the multi-layer property linking model; and creating a universal property identifier for each real estate property based on the final score. ### Claim 19 (depends on 18) The system of claim 18, wherein the final score determines if records in different data sources refer to the same real estate property. ### Claim 20 (depends on 11) The system of claim 11, wherein receiving the characteristic data further comprises: receiving characteristic data from a number of real estate-specific data sources; receiving tenant data from a number of non-real estate-specific data sources, wherein a subset of the non-real estate-specific data sources comprise tenant occupancy data not included in the real estate-specific data sources; constructing a multi-layer tenant linking model that correlates tenant occupancy data from the real estate-specific data sources and non-real estate-specific data sources; calculating a score for each layer of the multi-layer tenant linking model for each real estate property; calculating a final score from a weighted average of the scores for the layers of the multi-layer tenant linking model; and determining tenant occupancy for each real estate property according to the final score. ### Claim 21 A computer program product for automated property value calculation, the computer program product comprising: a non-volatile computer readable storage medium having program instructions stored thereon to perform the steps of: receiving historic financial transaction data for a first number of real estate properties over a specified time frame; receiving characteristic data regarding the first number of real estate properties, wherein the characteristic data comprises a number of defined categories; receiving historic demographic data for a number of demographic parameters over the specified time frame, wherein the demographic data corresponds to defined geographic regions in which the first number of real estate properties are located; build a predictive valuation model based on the financial transaction data, the characteristic data, and the demographic data; calculating individual values for a second number of real estate properties with the predictive valuation model according to characteristics of the second number of real estate properties; aggregating the individual values of the second number of real estate properties; receiving financial transaction data for the second number of real estate properties; and calculating a net asset value of the second number of real estate properties according to the aggregated valuations and financial transaction data.