What Is RELAR
Goals of the RELAR Analysis
Customization of the Product
Operation of the Product
Technology of the Product
Testing and Evaluation of the Product
Sample Report

What is RELAR

The Real Estate Liquidity Analysis Report is designed to determine the likely selling price and the amount of time required to sell an individual piece of residential real estate. RELAR does not directly address the issue of real estate “valuation” but instead focuses on using sophisticated mathematical methods to predict what is called the liquidity, the amount of money to be derived from the sale of real estate and the amount of time needed to obtain that amount.

The RELAR analysis provides an answer to a basic question about a property:

“If the property were put on the market today, how much would it sell for and how long would it take?”

This question poses the two elements of liquidity: the amount of money realized from a transaction and the amount of time required to obtain that money.

The Real Estate Liquidity Analysis Report uses statistical data analysis of real estate markets to predict both the expected selling price and the amount of time required for a sale for a given piece of residential real estate. The combination of expected sales price and expected time to sell is called liquidity. It is intended to answer the basic question: “If this house is put on the market now, how much will it sell for and how long will it take?”

The data analysis uses as input information from both public records databases and real estate sales databases. Public records databases provide title data while real estate sales databases (Multiple Listing Service databases) provide information about real estate offered for sale and the current state of a local real estate market.

In order to predict the sales price and sale time for a property, RELAR computes a value based on the current state of the market, as well as values at a time three months and six months in the past. This information is used to provide trend information allowing the value of the property to be predicted into the future. The time of the future prediction is estimated by modeling the behavior of buyers and sellers in the local market as shown by sales activity from MLS databases. MLS databases provide information about closed, pending, open and expired listings. The properties represented in each listing type provide a statistical view of the behavior of buyers and sellers in the local marketplace. In particular, the arrival of potential buyers at a particular property is modeled by a Poisson distribution. A Poisson distribution is a statistical model that provides a distribution of events where the interval between events is a uniform random function. Information from closed and pending listings is used to fit the parameters of the distribution and predict the probability that a buyer will have arrived at the property after a specified period of time.

The incorporation of time as an essential element of liquidity is unique to the RELAR system. It is also an essential component of understanding the actual monetized value of real estate. Time is a key factor, since the carrying costs for real estate during the sales process often represent a substantial fraction of the final amount to be realized from the sale.

Goals of the RELAR Analysis

The RELAR analysis was developed to meet the following goals:

RELAR is a web based product. In order to run a liquidity analysis on a residential property, the requestor logs into the site and is directed to the basic analysis page. This page is shown in figure 1.

A customer organization establishes an account and can then designate as many individuals as desired to be requestors. Each individual requestor has a separate user name and password for logging into the web site. When the requestor logs in, the customer organization is also identified for customization and billing purposes. Thus, if volume discounts have been established for a customer, all requestors using the RELAR will count toward the volumes required for specified discount levels. As described below, the ability to relate individual requestors to customer organizations also provides the ability to set default customizations for the RELAR based on customer preferences, and, where appropriate, to override those defaults to meet the specific needs of individual requestors within the organization. In either case, the customer organization still benefits from the total volume of requests in terms of pricing and discounts.

Using the query page, the requestor enters some basic information about the property. The minimum information required in order to perform the liquidity analysis is the street address and zip code (5 digit) of the property. Alternatively, the Assessor’s Parcel Number, along with either the zip code or the county can be used as the minimum information required to start the analysis.

Using basic property information, the software searches the title databases to find the specific property and retrieve all available information about the property. The information retrieved includes complete address and assessor information, property characteristics including house size, bedrooms, bathrooms, garages, pool, fireplaces, lot information, year built, and lien and transaction history information. Tax information is also available and retrieved at this point. A typical analysis results page is shown in figure 2.

The RELAR query page also offers an enhanced data entry capability about the property. Data can be entered regarding the information and characteristics that are retrieved from the title database. This provides the opportunity to check the information about the property based on information available from public records.
Of even greater value, it provides the capability to perform a liquidity analysis based on the property characteristics entered on the query page rather than those found in the public records database. This allows the liquidity analysis to be performed on properties where the property information available from public records may be inaccurate or non-existent. This includes cases where properties have been remodeled or where the liquidity analysis may be used speculatively to determine the potential value and liquidity of new construction or remodeling that has not yet taken place.

In addition to using the basic query page to request an analysis on a particular property, requestors have the capability of requesting bulk processing of lists of properties. Customers can download a program from the web site that will extract the relevant information from a spreadsheet containing a list of properties and create a file that can be uploaded to the web site. Once uploaded, each property in the file is processed through the liquidity analysis and a results file is created. The results file can be downloaded from the web site directly or emailed to the requestor once complete. The results can be viewed using the formatting program or exported to a spreadsheet for analysis and storage.

RELAR goes beyond the ability to run both basic and bulk queries on properties. Because RELAR uses trend analysis to predict property values and sales prices out to expected selling dates, RELAR must incorporate the ability to compute property liquidity at times in the past. As a result, RELAR can provide the ability to perform a historical liquidity analysis of a property. A historical analysis is performed by going to the historical query page and entering information similar to what is required for a basic liquidity analysis. Since the historical analysis capability is also incorporated into the bulk query feature, this can be used to determine the liquidity of a portfolio of properties at a specific time in the past. For example, a portfolio of properties could be analyzed as of the date representing the end of a fiscal quarter or year for an organization. Within a bulk query, it is also possible to provide a separate analysis date for each property in the portfolio. This can be valuable to government and law enforcement organizations seeking to understand the valuation and liquidity of properties used as the basis for loans at various times in the past.

Customization of the Product

Because the liquidity analysis is based on statistical data models, the analysis not only provides an estimate of the sales price and time to sell a property, but also provides information about the statistical data distributions underlying those estimates. This allows the values provided to be customized to the needs of the individual customers. For example, a customer might request the average expected sales price for a property. This value corresponds to the mean value of the Gaussian distribution representing the likely range of sales prices for the property. Since the sales price is modeled by a Gaussian distribution, and the moments of the distribution are also calculated, the result can be modified to provide a price representing other probable outcomes. For example, the mean value represents a price with a 50% confidence level. Using the moments of the distribution, other confidence levels can be calculated and provided for the customer. The customization can be done on a query by query basis with defaults provided for each customer and each requestor.

Similarly, time to sell is modeled by a Poisson distribution. This parameters of this distribution are determined by fitting the sale time data available from MLS databases, and confidence levels on sale time can also be computed.
Customers can thus specify results ranging from extremely conservative liquidity analysis (eg requiring a 90% confidence of achieving the price with 90% confidence on the time it will take) to more realistic models (90% price confidence in 50% confidence (average expected) time, to a model more representative of a typical appraisal model (50% confidence price representing average expected price in 50% confidence time representing average sales time. The choice will likely be driven by the use the customer is making of the analysis. The primary advantages to the customer are that the meaning of the results are well defined statistically and can be adjusted to the customer’s particular business and risk assessment models.

Similar customization is also available for the results of bulk queries.

Operation of the Product

Operationally, RELAR provides default customizations based on the preferences of particular customers and individual requestors. A customer may be an organization or individual who has established an account and provided a default customization profile. Each customer may designate multiple requestors, if desired. Each requestor established a user name and password, which provides the capability to override the default customizations to meet the needs and requirements of individual requestors within the organization. If requestors do not provide separate customizations, the default values supplied by the customer organization are used.

Technology of the Product

RELAR is implemented using Microsoft’s Internet Information Server as a web server and the Microsoft ASP.NET 2.0 framework as the basis for RELAR programming. This technology provides the ability to use the product in most standard web browsers, including Internet Explorer versions 6 and 7, Netscape Navigator version 8 and higher, Mozilla Firefox version 2 and higher and Apple MacIntosh Safari running under all versions of OS/x. This provides the ability to run a basic RELAR query using over 99% of desktop computer systems currently in use.

RELAR uses a number of processes and analysis methods which were developed specifically and uniquely for the RELAR product. As a result, CDR Business Solutions is applying for a number of patents on specific analysis methodologies and technologies incorporated into the RELAR product.

It is important to note that the actual analysis code, and thus the intellectual property associated with it, runs only on the web servers controlled by CDR Business Solutions. This limits the exposure of both patented and proprietary analysis techniques and methodologies and helps prevent either intentional theft or inadvertent disclosure of RELAR’s technologies to unauthorized individuals.
The RELAR technology and algorithms have been developed by CDR Business Solutions based on mathematically verifiable statistical data analysis techniques. These techniques include the use of multiple databases, the ability to cross-correlate among the multiple data sources available and the use of sophisticated data correlation and linear regression analysis methodologies to estimate and project both the value and liquidity of real estate based on the existing market data.

The figure below shows the use of multiple databases to support the CDR proprietary statistical analysis methods and algorithms.

Database Concept

The next figure shows the overall software design of the RELAR analysis system.

RELAR system

This diagram shows that the majority of the processing is performed on back-end web and database servers. The only basic requirements for the client computer used by the customer are a modern web browser and internet access.

It should be noted that in order to use the software needed to format files for bulk upload and analysis, the customer must be running a recent Microsoft operating system (Windows XP SP2 or later) along with the Microsoft Excel spreadsheet program to contain the initial list of properties.

Testing and Evaluation of the Product

The primary measure by which the RELAR product is evaluated internally is its ability to accurately predict sales price and market time for an individual property. One major issue is to determine the appropriate standard against which both RELAR and other products should be evaluated. In particular, CDR is reluctant to measure the results of a RELAR analysis by comparing the results to an appraisal or appraised value of a property. Appraisals represent opinions, albeit ones based on standards and experience, about the value of a property. While such a comparison may provide a gross indication of the accuracy and utility of the RELAR analysis, it is insufficient to provide the statistical accuracy evaluation many of our customers require as input to their risk analysis and valuation models.

Instead, our approach has been to evaluate the RELAR analysis against actual sales experience with lists and pools of properties.  We have compiled pools of properties representing over 3500 individual residential properties in the Southern California area. We are in the process of expanding those lists to include 20 major metropolitan areas nationwide.

The testing and evaluation criteria for the RELAR product, in order of priority, are as follows:

  
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