Abstract:
A system preprocesses property value estimates based on stored property data. It stores the computed property value estimates in a forecast data repository. The system obtains updates or new property data and computes new estimates according to a predetermined schedule. Users can instantaneously obtain property value estimates that have been preprocessed, thus eliminating a long wait period necessary for on-line processing.

Description:
RELATED APPLICATIONS 
     This application is based on a provisional application, Ser. No. 60/056,196 filed on Aug. 21, 1997, the contents of which are hereby incorporated by reference. This application is also a continuation-in-part of U.S. patent application, Ser. No. 08/730,289 entitled METHOD FOR COMBINING HOUSE PRICE FORECASTS, filed Oct. 11, 1996, which is hereby incorporated by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     The present invention relates generally to estimating property values, and more particularly, to providing preprocessed property value estimates. 
     Financial institutions and businesses involved with sales of property have long tried to estimate values of property accurately. Accurate estimation serves many important purposes. For example, financial institutions use property value estimates as one of the key factors in approving mortgage applications for real estate sales. Relying on the soundness of the estimate, financial institutions accept the risk of lending large sums of money and typically attach the property as security for the transaction. Accordingly, the accuracy of estimated value of the real estate entity is critical. 
     In addition to the accuracy of the estimate, timeliness is a significant factor. For example, a closing on a real estate sales contract may depend on the buyer successfully obtaining a loan within a limited time period. Also, the ability to evaluate the value of a large number of properties in a short time frame is a business necessity. Bids on pools of seasoned loans, for example, are due in a very tight timeframe and require collateral evaluation. Hence, the ability to estimate the value of the real estate entity quickly is very important to lenders and prospective buyers. 
     According to current industry practice, an estimate for a particular property is produced by a system in real-time, but this poses many problems. For one, the process is slow, especially for processing bulk transmissions. The system also needs to validate the address of the property to be estimated to ensure that the input address is indeed a valid address. For example, the system may check against an existing database to determine whether the input address contains a valid zip code or whether a combination of city and state fields of the input address matches the zip code of the input address. Moreover, real-time processing is subject to unpredictable conditions such as availability of properly running equipment, appraiser availability, and scheduling conflicts, whenever estimates are necessary. Further, a system computes an estimate for a property each time a request is submitted, even for properties that have been previously computed. Such redundancy wastes processing time and resources, results in repeated data entry, and increases the cost of the property valuations. 
     Furthermore, in existing systems, the platform providing the valuation estimates also needs access to “raw” bases. For example, the Hedonic model requires the capture of property level characteristic data and transaction value data to provide an estimate. This may involve either the use of large amounts of electronic data storage devices or connectivity with other systems, such as a data provider&#39;s system, which results in either additional cost and/or a reduction of reliability. In the case of the repeat sales model, after the growth rate table is created it is applied to the “seed value data set” to create, the forecasts. 
     Therefore, it is desirable to increase efficiency of property value forecasts by streamlining the forecasting process. 
     It is also desirable to provide a timely and reliable estimate of value. 
     SUMMARY OF THE INVENTION 
     Systems and method consistent with the present invention provide timely and accurate property value estimates by preprocessing the estimates and storing them for later access. 
     Specifically, a method consistent with the present invention of providing preprocessed property value estimates comprises several steps. Initially, the system accesses stored property data. The system then computes a property value estimates of the accessed property data. Thereafter, the system stores the computed property value estimates in a forecast data repository. 
     A system consistent with the present invention for providing preprocessed property value estimates includes accessing means, computing means, and storing means. The accessing means accesses stored property data with which the computing means computes a property value estimates of the retrieved property data. Thereafter, the storing means stores the computed property value estimates in a forecast data repository. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate the invention and together with the description, serve to explain the principles of the invention. 
       In the drawings, 
         FIG. 1  shows a property value estimation system consistent with one embodiment of the present invention; 
         FIG. 2  shows a schematic overview for creation of the forecast data repository consistent with one embodiment of the present invention; 
         FIGS. 3   a - 3   c  illustrate a sample format of the property data used in Hedonic model estimation; 
         FIG. 4  illustrates a sample format of the sales transaction data; 
         FIG. 5  shows three sample input screens of a user terminal; 
         FIG. 6  shows a sample output screen of a user terminal; 
         FIG. 7  is a flowchart illustrating the process for creating a new master property file for a Hedonic model; 
         FIG. 8  is a flowchart illustrating the process for creating a new master sales transaction file for a Repeat Sales model; 
         FIG. 9  is a flowchart illustrating transaction data creation performed for a Repeat Sales model; 
         FIG. 10  shows a sample layout of the fields contained in seed value file and repeat sales file; 
         FIG. 11  is a flowchart illustrating the process for determining aggregate property is value activity by property types; 
         FIG. 12  illustrates a sample layout of growth rate tables by zip code and by county; 
         FIG. 13  shows a table illustrating representative fields of the records stored in a forecast data repository; and 
         FIG. 14  is a flowchart illustrating data delivery to third parties. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENT 
     Reference will now be made in detail to the present preferred embodiment of the invention, an example of which is illustrated in the accompanying drawings. Where appropriate, the same reference numerals refer to the same or similar elements. The appended claims define the scope of the invention; the following description does not limit that scope. 
       FIG. 1  shows a property value estimation system  100  consistent with the present invention. System  100  includes a computer  110 , a property database  120 , a forecast data repository  130 , and a user terminal  140 . In one embodiment consistent with the present invention, computer  110  includes a mainframe for managing the property data and a UNIX machine for processing the property data to compute property value estimates. The mainframe may be an Amdahl processor primarily using tape storage in addition to having approximately 10 Gb of DASD (Direct Access Storage Device). The UNIX-specific instructions may be executed on a Sun SPARC® 1000e with the Solaris®2.5.1 operating system, three system boards, six CPUs, 768 Mb of memory, and 306 Gb of disk space. One skilled in the art may, however, use any computing system with adequate processing and memory capabilities. 
     A program  115  controls the overall process of computer  110  and may be written using such tools as COBOL II, SAS, and IBM utilities (Syncsort, JCL, FTP, etc.). To perform some standard functions, program  115  may use various commercial software such as Group 1 (Code 1 Plus v1.5 and Demographics v2.7), MathSoft (S-Plus v3.4), and the SAS Institute (mainframe and UNIX v6.12). The use of these programs will be explained in detail below. 
     Unlike previous systems that computed property value estimates on-line, system  100  offers an efficient and flexible way of providing property value estimates. System  100  provides a collection of preprocessed property value estimates, which can be accessed to retrieve estimates of a property quickly. Program  115  can compute the property value estimates using any one or a combination of methods. 
       FIG. 2  shows a schematic overview of one embodiment consistent with the present invention. System  100  first accesses an address batch  210  representing property data of a portion or the entire country stored in property database  120 , and provides the property addresses and related data for various statistical models. 
     In one embodiment consistent with the present invention, program  115  computes the estimate of property values using two statistical models, i.e., Repeat Sales model  220  and Hedonic model  230 , and combines the results to produce the best estimate. Other models such as HNC model  240  and Value Point model  250  can also be used to compute the property value estimates. An example of combining results of the statistical models is provided in the above-referenced related application Ser. No. 08/730,289 For explanatory purposes, the described embodiment will combine the property value estimates of Repeat Sales model  220  and Hedonic model  230 . One skilled in the art may use any one or a combination of methods without departing from the spirit of this invention. Regardless of what model or a combination of models are used, the property value estimates are stored in forecast data repository  130  for later retrieval. One skilled in the art may also design a system to store the property value estimates in database  120 , which stores the property data. 
     Property database  120  stores data representing address batch  210  including property data and sales transaction data for use in computing the property value  20  estimate. A property data table  300  of  FIGS. 3   a - 3   c  illustrate a sample format of the property data. Property data table  300  is composed of a field name column  310  containing address and county information, type column  320  indicating whether the field name contains alphabetic characters or numbers, length column  330  indicating the size of the field, and details/comments column  340 . 
     A sales transaction, data table  400  of  FIG. 4  illustrates a sample format of the sales transaction data including a key column  410 , a field name column  420 , a type column  430 , a length column  440 , and a comments/value range column  450 . Key column  410  indicates with a check mark whether program  115  can sort the records based on the corresponding field name. The property data can be created by entities maintaining system  100  or obtained from commercial data providers such as DataQuick according to a predetermined schedule. In one embodiment consistent with the present invention, system  100  receives property data on a monthly basis from the data provider, typically on tape drives. System  100  stores the property data in property database  120  and updates the property data according to a predetermined schedule, e.g., every three months, to computer  110 . One skilled in the art may easily vary the frequency and the period of the predetermined schedule at any time. For example, the predetermined schedule may be based on a time period or an occurrence of a certain event. In another embodiment consistent with the present invention, system  100  uploads the property data to computer  110  on a monthly basis as new property data files are received. One skilled in the art may, however, easily modify the frequency of data updates and the storage location. 
     Forecast data repository  130  contains property value estimates for each property stored in property database  120 . As will be explained in detail below, program  115  may compute the property value estimates using a single statistical model or a combination of models. Computer  110  can also periodically update property value estimates, for example, every three months or whenever the property data is updated. 
     User terminal  140  can be a dummy terminal or a personal computer connected to computer  110 . Through user terminal  140 , users can access preprocessed property value estimates for any given property. For example,  FIG. 5  shows three sample screens of user terminal  140 . An initial screen  510  presents a menu of options the user can choose from. If the user selects option “S” to initiate the Home Value Estimator, for example, user terminal  140  presents a greeting screen  520  that welcomes the user and gives an option to continue or exit to the main menu. If the user enters “Y” to continue, user terminal  140  presents a data entry screen  530  requesting information or address of property for which the user would like an estimate. Thereafter, computer  110  accesses forecast data repository  130  to obtain the property value estimate corresponding to the data entered by the user. 
       FIG. 6  shows a sample output screen containing data such as property ID information, property value estimate, price trend, recent sale data, and area map. One skilled in the art may, however, easily modify the input and output screens. 
     As mentioned above, the described embodiment will be explained in detail assuming system  100  combines the results of Repeat Sales model  220  and Hedonic model  230  for computing the property value estimates. Also for explanatory purposes, system  100  updates property database  120  and computes new estimates on three-month cycles. 
       FIGS. 7 and 8  illustrate processes, preferably performed by the mainframe portion of computer  110 , relating to a data warehouse platform. Specifically,  FIG. 7  is a flowchart illustrating the process for creating a new master property file for Hedonic model  220 . Initially, program  115  retrieves and consolidates backup DP (data provider) files for months  1 ,  2 , and  3  stored in property database  120  (step  705 ). Program  115  also copies into memory the previous quarter&#39;s master property file, which program  115  used for computing previous quarter&#39;s property value estimates (step  710 ). Program  115  then identifies counties contained in the new property file and replaces the existing property data for each county with the updates (step  715 ), thus creating a new DP master property file (step  720 ). 
     Program  115  then “scrubs” addresses for each record in the new master property file to obtain a standardized version of each property address (step  725 ). During the scrubbing process, program  115  may use commercial software, such as Group 1 &#39;s Code 1 Plus and Demographic Coding System. Program  115  inserts the scrubbed address information in a corresponding field of property data table  300 . These steps are repeated until program  115  reaches the end of the master property file. Program  115  thus produces a scrubbed property flat file (step  730 ). 
     To accommodate the process of Hedonic model  230 , program  115  cuts the single large scrubbed property flat file into smaller county-specific files (step  735 ). Specifically, program  115  reads the scrubbed property flat file and creates a county-specific version of the flat file for each unique county encountered. Program  115  repeats these steps until it reaches the end of the scrubbed property flat file. Program  115  then transfers the resultant county-specific versions of the scrubbed property flat file to the UNIX portion of computer  115  where model processing will occur. A commercial package, such as File Transfer Protocol “FTP” utility by IBM, may be used to transfer the file. If program  115  encounters any errors during the cutting, scrubbing, or FTP process, program  115  generates an error report (step  740 ). 
       FIG. 8  is a flowchart illustrating the process for creating a new master property file, preferably performed by the mainframe portion of computer  110 , for Repeat Sales model  220 . The processes similar to that of one shown in FIG.  7 . Initially, program  115  retrieves and consolidates sales transaction files for months  1 ,  2 , and  3  stored in property database  120  (step  805 ). Program  115  also copies into memory previous quarter&#39;s sales transactions file, which program  115  used for computing previous quarter&#39;s property value estimates (step  810 ). Program  115  then identifies and appends any new sales to an existing property record in sales transaction data table  400  (step  815 ). These steps are repeated for each property record until program  115  reaches the end of the sales transaction file, thus creating a new sales transactions file (step  820 ). 
       FIG. 9  is a flowchart illustrating transaction data creation performed for Repeat Sales model  210 . In general, the process of  FIG. 9  identifies redundancies between the data provider, and loan agency data, e.g., FM/FNM (Freddie Mac and Fannie Mae), to create the most accurate record for a given property. To do so, program  115  compares the new sales transaction file against agency data to identify and resolve data redundancies (step  905 ). Program  115  then scrubs the addresses contained within the file and merges the unique agency and data provider data to create a larger temporary sales transaction file (step  910 ). As mentioned above, scrubbing may be performed using commercial software such as Group 1&#39;s Code-1 Plus and Demographics Coding System. From the temporary sales transaction file, program  115  generates two separate output files: seed value file containing most current sales price (step  915 ) and repeat sales file containing the scrubbed property address with all repeat sales transactions (step  920 ). Tables  1010  and  1020  of  FIG. 10  shows a sample layout of the fields contained in seed value file and repeat sales file, respectively. Program  115  transfers seed value file and repeat sales file to UNIX portion of computer  110  for processing (steps  925  and  930 ). 
     The next phase of program  115  relates to CAD (Collateral Assessment Development) processes. In one embodiment consistent with the present invention, program  115  operates on a UNIX platform with SAS software. As shown in  FIG. 11 , program  115  uses repeat sales file from  FIG. 9  (step  1105 ) to run the Repeat Sales model  220  to determine aggregate property value activity (e.g., increase or decrease in value) by property types (e.g., condos vs: single-family) within zip codes and counties for a given period of time (step  1110 ). To do so, program  115  analyzes the repeat sales file and groups the records by property type for each unique zip code and county. Program  115  then calculates the aggregate appreciation/depreciation in property value for the grouped sample. Program  115  may also create plot of indices to enable visual, inspection for quality control using commercial software such as MathSoft&#39;s S-Plus. Program  115  then produces a GRT (Growth Rate Table) or GMT (Growth Multiplier Table) by zip code  1210  and a GRT by county  1220 , a sample layout of which are illustrated in,  FIG. 12  (step  1115 ). GRT  1210  and  1220 , in conjunction with a property&#39;s seed value, provide a market-to-market value of the property within a specified period of time. 
     Program  115  takes GRT  1210  and  1220  and applies it to records contained in the seed value file (step  1120 ) to create a RS (Repeat Sales) forecast (step  1125 ). A detailed explanation of Repeat Sales model  210  is provided in the above-referenced related application Ser. No. 08/730,289. This step produces a seed value file with estimated current value based on repeat sales. 
     Program  115  also takes county-specific versions of the scrubbed property flat file (step  1130 ) and merges the DP (Data Provider) records with repeat sales forecast information (step  1135 ) to obtain all data provider/FM/FNM records with most recent sale price and property data, preferably by county (step  1140 ). Program  115  then estimates county level estimate using Hedonic model  220  (step  1145 ) for all properties in the above-referenced related application Ser. No. 08/730,289 
     Program  115  applies Hedonic model  220  to all properties in the county (step  1150 ) and produces a data provider/agency properties file at the county level with hedonic estimate of value (step  1155 ). Program  115  then combines the file with Hedonic model  230  estimates of value with seed value file containing Repeat Sales model  220  estimated current value and generates the combined LP (Loan Prospector) estimate (step  1160 ). A detailed explanation of combining the results of the statistical models is provided in the above-referenced related application Ser. No. 08/730,289 Program  115  applies previously stored forecast data records from forecast data repository  130  (step  1165 ) and updates forecast data repository  130  with new property value estimates (step  1170 ).  FIG. 13  shows a table  1310  illustrating representative fields of the records stored in forecast data repository. Once the UNIX portion of computer  110  completes processing to obtain property value estimates, program  115  transmits table  1310  and GRT tables  1210  and  1220  to the mainframe portion of computer  110  for data delivery to third parties (steps  1175  and  1180 ). 
     As shown in  FIG. 14 , records stored in forecast data repository  130  (step  1410 ) may be delivered to commercial data providers (step  1415 ) or be used to generate a report for a specific property (step  1420 ). GRT tables  1210  and  1220  (step  1425 ) can also be used to generate a growth rate report (step  1430 ) or be delivered to third parties (step  1435 ). 
     Users at user terminal  140  can now immediately access property value estimates by entering the input information of the property to be estimated. Once program  115  receives a request from user terminal  140 , program  115  can access the preprocessed property value estimate and display the results in an output screen such as one shown in FIG.  6 . 
     Conclusion 
     Systems and methods consistent with the present invention improve the efficiency of estimates of value for real estate entities by preprocessing the estimates and storing them for later access. Such systems and methods periodically process estimates of property value and store the estimates in a central repository. The central repository is periodically updated with new and revised property estimates. 
     It will be apparent to those skilled in the art that various modifications and variations can be made in the systems and methods of the present invention without departing from the scope or spirit of the invention. Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the invention indicated by the following claims.