Anandalingam, G. & Chen, L., “Linear Combination of Forecasts: A General Bayesian Model”, J. of Forecasting, July/September 1989, 8(3), pp. 199-214. cited by other.
The present invention relates generally to a computer implemented method and system for the automated selection of real estate listing data and the processing of the data into a presentation and proposal format, and more particularly to a computer implemented method and system for selecting and processing the information through a user/server environment accessed through a Web site. A result of the invention on a national scale delivers reports of national, regional, and state real estate activity on demand for executive users.
Real estate agents invest hours of their time searching Multiple Listing Service databases for data pertaining to real estate seller or buyer criteria. They search and compile data for either client presentations to attract new clients or update data to serve existing clients. While the overall demographics of real estate agents range from very young to mature, most agents tend to skew between the ages of 35 to 65 years of age. Most agents are less computer literate than more computer literate and lack the expertise necessary to perform statistical evaluations, projections, and graphic representations of the data, especially in an industry where time is of the essence. MyRealEstats delivers in minutes what an average real estate agent would require many hours to accomplish if the agent had the tools and training necessary to generate the outputs.
The Multiple Listing Service data base includes all or most of the data necessary to prepare for client presentations and research, but it can be difficult for users who do not have a comfort level with computer systems to generate the correct outputs and to format outputted data into reports, charts and graphic representations. Even though most real estate offices have programs available such as Microsoft Excel and PowerPoint, few agents have the skill sets necessary to utilize and program these tools to work with the Multiple Listing Service data.
Broadcasting outlets have similar issues. Audience measurement companies such as Arbitron survey markets across the country to identify listenership, but the data must be processed based on buyer and seller criteria to produce information. Data by itself is not information until it is in a format enabling the user to properly evaluate it. There are a number of systems that mine Arbitron data, analyze the data, and format it into presentation and proposal formats; Tapscan, Strata, and Arbitrends are prime examples. These programs were all designed to serve the needs of both broadcast advertising buyers and sellers. There exist a correlation between the activities of broadcast account executives and real estate agents. Both access sophisticated and complex databases in order to search the needs and requirements of their clients. Real Estate agents, unlike Broadcast Account Executives, do not have a resource or tool similar to Tapscan, Strata, or Arbitrends. The approximate 1,000,000 or more US real estate agents do not have a single system available to simplify the process of accessing, analyzing, organizing and formatting their Multiple Listing Service data into information and knowledge as do the estimated 50,000 radio account executives in the US.
The subject invention is directed to a computer implemented method and system which contains modules that assist in residential and commercial real estate transactions from the early buyer/seller “what if” scenarios to ratification of the contract. The first module calculates the closing or settlement costs charged to real estate buyers or sellers to approximate the amount of cash required at “Closing” or “Settlement” by the buyer to complete the transaction, and the amount of cash, if any, the seller will net from the sell of the property. Calculations begin with the end of the transaction process to confirm that the buyer's budget is realistic and the homes considered are within the buyer's ability to purchase. Variables associated with the seller are calculated to assure that pricing and selling the property yield anticipated results. Beyond the initial “Settlement Estimates” the invention's second module furthers the process by providing methods and systems for the automated selection, aggregation, capture, analysis, and presentation of residential and commercial real estate information to assist in the completion of a real estate transaction. The information is accessed from a variety of sources including but not limited to location aware data such as neighborhood market trending and one to many multiple listing services accessed licensed real estate agents, financial institutions, mortgage and investment lenders, media, and government agencies, and others in a user/server environment accessed through a Web site
The detailed description will refer to the following embodiments, wherein like numerals refer to like elements, and wherein:
Described herein are methods and systems for providing integrated components to assist buyers and sellers of real estate in understanding multiple considerations when buying or selling real estate property. Informed buyers and sellers of real property increase the probability for the highest current demand value realized between all parties at any point in time. The system, Real-E-Stats, is a computer implemented software invention, which contains several modules including but not limited to: Settlement Estimates, Loan Point Buy Down Buyer or Seller Negotiation Estimates, Yield Management Forward and Backward Pricing, Listing, and Offer Engines, Automated Buyer, Seller, Renter, and Prospecting Data Capture and Processing Presentation Tools, a Property Valuation Index (PVI), and GPS Location and Data Delivery on Demand. The module “Real-E-Stats Settlement Estimates™” calculates the closing or settlement costs charged to real estate buyers or sellers to approximate the amount of cash required at “Closing” or “Settlement” by the buyer to complete the transaction, and the amount of cash, if any, the seller will net from the sell of the property. The invention minimizes the amount of information provided by a user while maximizing the output received to arrive at estimates that can be used to support the marketing and pricing of a seller's house, and enable buyers to plan around the economics, pricing, and fees associated with purchasing a house in order to negotiate the transaction and arrive at the settlement table with the cash necessary to complete the transaction. The Data Correction Module (DCM) over see's the data entries the user provides and offers mathematical corrections or considerations to verify the credibility of user entry. Modules in Real E-Stats require yield sensitive pricing calculations. The yield management application module provides unique pricing algorithms to present buyers and sellers multiple scenarios of pricing, value and offers based on property listing and value inputs and market demand fluctuations. The Property Value Index module provides multiple variable index processing including property active listings, contract, sold, tax assessment, annual taxes, total and living square footage, and time or days on the market to provide users with value projections that go beyond any single means of comparative analysis. To fully complete the buying and selling process, the invention connects the users to a variety of data sources. Multiple Listing Services (MLS) are widely used by members of the real estate industry as regional repositories of inventory. This invention uses this data in a variety of modules. Access of data is provided through a request transmitted to a real estate Multiple Listing Service (MLS) for processing and is returned to the system Web site or local computer application for presentation analysis and processing. The system compiles, aggregates, and organizes the data into a UI presentation including data reports, graphic displays, data explanations, data summaries, valuations and property value indexes and valuations. The agent selects print, file, or e-mail options for transmitting, delivering, or presenting the information to buyers, sellers or investors to support the client's decision-making process. The agent stores the processed information and system outputs on the Web site or local computer application for reference, updating, or comparative analysis. The component value of the system spans the entire selling and buying process from seller pricing, buyer qualification, prospecting, evaluation, offers and negotiations, serving the specific needs of sellers and buyers through the efforts of an agent to arrive at the most efficient and effective valuation and sale of residential or commercial real estate.
The premise for the Settlement Estimates module began as a request of an Agent by a Seller for an estimate of net cash after closing. Since there was not a simple way to prepare a document for estimating settlement charges, each individual fee and charge was researched and entered into a spreadsheet and the charges and credits were added and subtracted to arrive at a very rough estimate of seller net revenue. Understanding that the taxes charged by the county (Montgomery County, Maryland) varied according to the amount of the sale, and settlement dates impacted the amount of pre-paid expenses recaptured by the seller, complexity was added to the task of calculating a realistic estimate. It required much more time and effort than anticipated. Research identifying the input values to apply to the transaction cost required formulas to calculate county transfer taxes and credits. Hours later an estimate was available for the client. It had required a computer, internet connection, research, analytical skills and, programming and formatting skills, and most of all, valuable time to prepare the report in an industry where time is of the essence. Another agent saw the output report and asked if there was anything like it available for real estate buyers and investors. The need for a System enabling quick and easy access to settlement estimates for sellers, buyers, and investors of real estate had been identified by need. Research for an existing tool identified a void in the transaction process of selling and buying real estate. Two types of real estate transactional analysis existed, but neither served the initial need of either buyers or sellers; RESPA, the Real Estate Settlement Procedure Act required “Good Faith Estimates” prepared by mortgage companies when a loan application is received. The RESPA “Good Faith Estimate” is prepared only after the mortgage application is made. RESPA estimates are not prepared when Buyers request what is commonly referred to in real estate as a “Pre-Approval Letter” for financing. The “Pre-Approval” letter states that the buyer or buyers have been pre-qualified for a loan. It is not a guarantee for a loan, but is used and often attached to the contractual offer as an indication of financing approval in advance of application. The second transactional analysis is the HUD-1. It is the actual posting of charges and fees prepared by the Settlement Company, Agent, or Attorney prior to Settlement and accurate to the date of settlement or closing. Every fee and charge for both Buyer and Seller is included on the HUD-1, but the HUD-1 is delivered 24 to 72 hours prior to Settlement. From the time that a seller decides to sell and a buyer decides to buy, through all of the research, house hunting, Open House and appointment visits, and the compilation of comparative market analysis, through the offer, negotiation and ratification of the contract, there is a void of Settlement Estimates which enable sellers to do a better job of pricing their home, and buyer's a better job of qualification and negotiation. Whether negotiating for lower prices, subsidies, loan point discounts or improvements, Settlement Estimates enable Agents to educate their clients on what it takes to buy a home, and how much they will net when selling a property.
The Mortgage and Points Calculator
Ask an agent, buyer, or seller to explain :Buy Down Loan Points” and the answer is that they really aren't sure how they work. Agents, buyers and sellers know that buying down the mortgage rate through loan points reduce interest payments on mortgages but most cannot calculate the savings. Real-E=Stats answers the question with little input from the user. Users need only enter the length of the loan in years and the first month of the year that the mortgage payments commence. The Mortgage Calculator retrieves the mortgage amount from the buyer or seller input fields and calculates the monthly and annual mortgage payments. Users select “Loan Points” from a drop down menu and the system calculates the reduction in the mortgage interest rate and the reduction in monthly, annual, and total mortgage payments over the life of the loan. The value of the Mortgage and Points Calculator benefits the buyer by clearly demonstrating short and long term savings. It provides information useful to both the buying and selling agents on a negotiable benefit toward completing the contract. Sellers may agree to buy down the mortgage rates to make the property more attractive and affordable to the buyer. Buyers may use loan point buy downs in the contract negotiation to reduce monthly payments. The system enables users to calculate the value of advance interest payments and the amount of time necessary to recapture the investment. While mortgage calculators are not unique, combining the mortgage calculator with the Loan Point calculator offers a unique and automated tool to the transaction process. It requires no previous knowledge of the calculations or benefits that buying down the mortgage rate delivers to buyers, and offers users a valuable tool in negotiating the offer and contract.
Yield Management may be defined as getting the very best price at a specific point in time based on the current demand in a marketplace for a fixed inventory. Yield Management is used extensively in the marketing of broadcast inventory, hotel rooms, rental cars, cruise ship packages, and airline seats. As applied in the system, Yield Management is for the first time presented as a pricing tool or engine for determining the list price of a real estate property based on value, and anticipating or projecting the offer price. Yield Management, as used in the system, is expanded to work both forward and backward, a uniquely powerful and creative adaptation of the economic variables associated with the marketing and purchase of a property. Forward working, it converts the calculated value of a property to a demand curve that calculates a number of property demand values, listing prices, and anticipated offers. If demand is high, the value of a property increases as do the list prices and anticipated offer prices. High demand is often referred to in real estate as a “Seller's Market”. When demand is low, a “Buyer's Market”, values decrease as do the list and offer prices.
The first application of Yield Management, the forward adaptation, uses a system property value generated from a unique combination of indexing to create three demand curves or pricing grids; the Value Price, the List Price, and the Offer Price. The Offer Price is a projected sales price or property value anticipated by the Seller that begins the contract negotiation. Depending on the current demand in the marketplace, the Offer Price could be low, moderate or high. The system generated property value calculated by applying the PVI (Property Value Index) to the property List Price, considers eight weighted variables to determine value and demand:
A new application of Yield Management, the backward application begins with the list price. It is the tool designed for the buyer. Utilizing the same PVI variables that the seller used in determining value to arrive at a list price, the backward application uses the list price to generate value and offers. Real-E=Stats itself is not a negotiation tool, but it generates the knowledge base and information that is used in the negotiation of the transaction between buyer and seller.
A third element is incorporated into the unique application of yield management to the real estate. Selling and buying a property is an emotional decision that often includes subjective variables. The impetus to make or accept an offer may not always be logical or analytical. Real-E-Stats and the pricing engine include a subjective module, the Value Adjustment Variable. The Value Adjustment Variable is a list of features ranging from location, curb appeal, room sizes, and other attributes that are each subjectively valued by the user to add or subtract from the computational value of the property. It can increase or decrease the property value based solely on the subjective interpretation of the user, but is ultimately designed to reduce the emotional impact of selling and buying by evaluating the incremental values of the property. The adjusted value is automatically incorporated by the system into the demand curve or price and value grids adjusting the outputs. MyRealEstats is a sub module of the Real-E-Stats system. MyRealEstats includes the following functionality:
1. Selling Module
2. The Buying Module
3. The Rental Module
4. Prospecting Module
5. Financing and Home Services
6. Wireless Links
In order to more fully understand the subject invention, below is a list of abbreviations and definitions that are used in the real estate industry.
Absorption Rate—the amount of inventory available for sale in a defined area divided by the amount of properties currently under contract in the defined area which yield the amount of time in months that it will take to sell the remaining unsold inventory. Absorption Rate identifies the demand for houses in a defined area.
Active—houses currently for sale
Advertised Subdivision—the name the area or community or development where a house is located
Agent—A Real Estate Agent or Salesperson
Assessment—the County assessed tax value of a property used to calculate taxes
BSMT—Basement
BR—Bedrooms in a house
Close Date—the date of settlement
Comps—Comparable houses
Contract—houses that are currently listed with accepted offers that have not yet gone to settlement
DOM—Days on Market—the amount of days that a house has been active for sale, or the amount of days that the house was on the market prior to being sold.
Sold—houses that have been listed, contracted for sale, closed.
Expired—properties whose listing has reached its end date and are no longer actives for sale
FB—Full Baths
Foreclosure—The lending institution's exercise of right to take ownership of a property due to lack of payment by the mortgagee.
FP—Fireplace
HB—Half Baths
HVAC—Heating, Ventilation and Air Conditioning
List Price—the asking price of a house listed for sale
Lot and Block—legal descriptions of the location of a property
LV—levels in a house including the basement
Multiple Listing Service (MLS)—The organization that captures property listings and maintains records of sales, withdrawals, and expirations of real property.
Settlement—Sometimes referred to as Closing, it the process where documents are signed and monies are exchanged in the sell and purchase of a property.
Short Sale—An owner who can no longer make mortgage payments has negotiated the sale of a property with the mortgage company at a price less than the outstanding mortgage to retire the debt. Short sales require third party approval.
Sold Price—the final Sales Price of a house
Third Party Approval—A Short Sale or Foreclosure that requires a third party, normally a bank or legal representative of financial company to approve or accept an offer to purchase.
Yield or Revenue Management—the process of generating the most money for a good or service based on the current state of the market demand.
Withdrawn—listings where sellers have requested that their properties be taken off the market for sale
This application claims the priority of U.S. Provisional Application Ser. No. 61/118,422 entitled “A computer implemented method and system, Real E-Stats, for the automated selection, aggregation, capture, analysis, and presentation of residential and commercial real estate information accessed from one to many or all multiple listing services for licensed real estate agents, buyers or sellers of real estate, financial institutions, mortgage and investment lenders, media, and government agencies,” filed Nov. 26, 2008. REFERENCES CITED4,429,385January 1984Cichelli, et al4,870,576September 1989Tornetto5,006,998April 1991Yasunobu, et al5,032,989July 1991Tornetta5,124,909June 1992Blakely, et al5,235,673August 1993Austvold, et al5,235,680August 1993Bijnagte5,237,157August 1993Kaplan5,309,355May 1994Lockwood5,361,195November 1994Shoquist, et al5,361,201November 1994Jost, et al5,414,621May 1995Hough5,452,468September 1995Peterson5,500,793March 1996Deming, Jr., et al5,573,747April 1071Adams5,584,025December 1996Keithley, et al5,664,115September 1997Fraser5,680,305October 1997Apgar, IV5,857,174January 1999Dugan5,960,407September 1999Vivona6,115,694September 2000Cheetham, et al6,141,684October 2000Bonissone, et al6,178,406January 2001Cheetham. Et al6,236,977May 2001Verba, et al6,401,070June 2002McManus, et al6,435,303September 2002Li6,484,176November 2002Sealand, et al6,519,618February 2003Snyder6,594,633July 2003Broerman6,609,109August 2003Bradley, et al6,609,118August 2003Khedkar, et al6,684,196January 2004Mini, et al6,748,369June 2004Khedkar, et al6,842,738January 2005Bradley, et al6,871,146March 2005Florance, et al6,883,002April 2005Faudman7,249,146July 2007Brecher7,305,328December 2007Fleming, et al20040143450July 2004Vidali
Number | Date | Country | |
---|---|---|---|
61118422 | Nov 2008 | US |