Property At The Appraised Value example essay topic

Abstract The author was recently asked to make a presentation on the use of geographic information systems, commonly referred to as GIS, to a local chapter of the Appraisal Institute. The purpose was to show how an appraiser or appraisal reviewer could use GIS to find cases of white collar real estate scam. The items discussed at that presentation are covered in further detail in this paper. Appraisers need to show the reasoning behind their value opinions by discussing important spatial relationships and their likely effect on value. Geographic information systems (GIS) can be used to analyze these relationships and to show why a client should select an appraiser who has this level of information. Gilbert Castle has noted that real estate is essentially a game of information arbitrage.

The likely winner of the game is the person that takes advantage of computerized analyses. Castle explains that GIS is an attention-getting way of showing what you know. (n 1) Of course, larger data sets are used for GIS analysis, not just the minimum 'three comps. ' The visual aids that GIS can generate could also be very useful in litigation, to help explain complex issues to a jury that is relatively unfamiliar with real estate valuation. Clear communication of complex technical issues is the basis of forensic consulting, an emerging field that is expected to grow more rapidly in the future. The need for forensic consulting has been created by rapid changes in technology. The Arden-Guthrie Problem Arden-Guthrie is a neighborhood in San Bernardino, California.

A number of fraudulent transactions in that neighborhood inflated the ostensible value of local quadruple properties. The question is, How could a reviewer have used GIS to find the problems caused by the fraudulent sales? Many of the properties in question are located within the block group outlined in red in Figure 1. Other problem properties are located in a block group just south of the outlined area. The larger red area at the top of the map is part of a color-coding system that shows median rents by census block groups.

As we can see, renters in this area one-half mile to the north were paying from $913 to $1,001 per month at the time of the 1990 census. This represents the highest rent category for San Bernardino County. Rental data from the 2000 census will be available soon. A reviewer could print out such a map and use it to check quickly for inconsistencies. One obvious inconsistency would be an appraisal that concludes that rents in the highest bracket are indicated for a property that is located in a low-rent area.

Census data is relatively inexpensive. Data for the entire country was available for less than $500 more than five years ago. The appraiser's first step is to define the proper market area to search. Once this is done, sales data can be downloaded from a time-sharing data service. The data retrieved can be imported into a GIS system and then mapped to show the range in prices. One common method is to search within a one-mile radius of the subject property, but an appraiser using this method would retrieve data from the high-income area.

Another method is to search by zip codes, in this case 92404. The heavy blue line on the map in Figure 2 shows the resultant boundary. As we can see, searching by zip code also results in too broad of a search. Searching by block groups would provide much more detail, but this type of search is not available using data from typical real estate data services.

Searching by census tract provides the best results. Census tracts are indicated by the heavier black boundaries. Zip code and block group boundary files typically have to be purchased separately. Updated, high-resolution zip code boundary files for California cost approximately $500. It costs only a few dollars to export the data, but you have to pay an annual fee to use the system. Basic GIS mapping systems for PCs typically cost more than $1,000, but newer, more limited products cost much less.

You can display the data with the less expensive GIS programs, but you won't be able to do the analyses shown later in this article. A tremendous amount of other data can also be mapped. Some is available for free via the Internet; other data sets can cost thousands of dollars per year for the database and a similar amount for each of the annual updates. The data has to be geo coded after it has been downloaded. This is the process of adding latitude and longitude coordinates to each of the records so that the data can be displayed on electronic maps. This is the most time-consuming and expensive part of the analysis.

Geocoding the addresses yourself requires street files, and street files can range in price from $500 to $10,000. Only the most expensive street files have the most recent streets, so finding properties in newer subdivisions has been problematic. This situation may change in the near future. Data services have latitudes and longitudes for each property, but many will not sell them. This may be because they want to entice you to pay an extra $250 per county each year to create simplistic maps with their mapping system. It will typically cost an extra 10% of your subscription price to obtain lat / long coordinates that are available for other property types from other data services.

Once the data has been imported into the GIS, geo coded, and mapped, the inconsistencies are readily apparent. The ranges in sale prices are labeled in the legend in the upper right corner of the map in Figure 3. The subject neighborhood looks like a rainbow, with the properties shown in red selling at the highest prices, other properties selling for half as much, and still others selling for only one-fifth of the highest-priced ones. How often does an appraiser find such a range in prices? Within one block, some properties are selling at prices 25 times higher than other properties. Another effective method of conveying the diversity in prices is by graphing.

Most advanced GIS packages allow the analyst to graph the data that has been mapped. Quadruplex es in the area come in four sizes, as shown on the horizontal axis in Figure 4. As the graph shows, there is a phenomenal range of prices for units of the same size. The typical relationship is for increases in sale prices to taper off as the size of the home increases. Figure 5 shows that this neighborhood is not typical. This graph compares the prices of resale housing (on the left) to the sale prices of new housing (on the right) located in the same block group.

How Market Analysis Can Prevent Problem Appraisals It is obvious that fraudulent transfers can result in problem appraisals that indicate erroneously high values. It may be less evident that the cause of problem appraisals is a lack of proper market analysis. The examples that follow demonstrate why the FDIC used to order two appraisals for its larger assets. In the past, these appraisals were only reviewed when values exceeded $2 million and when the appraised values varied by more than 20%. Appraisals of land presented the biggest problem, with appraised values often varying by more than 50%.

These values could have been reconciled more accurately if the appraisers had shown the reasoning behind their opinions. Example 1. Appraisal of Residential Acreage Consider a large land holding that was appraised at values of approximately $7 million and $48 million. The lower value resulted from an appraiser using recent 'comparable's ales from far outside the area.

The higher value resulted from another appraiser using older data from a superior area nearby (like the red-shaded area in the Arden-Guthrie map). GIS could have been very useful in reconciling these divergent value opinions at the FDIC as part of an appraisal review. The review appraisers were expected to reconcile these property values and judge which value was the most accurate. However, GIS was not available, so the property had to be valued by an appraiser experienced in market analysis. The market study showed the irrelevance of the data that the original appraisers used. The appraised value based on the market study was supported by a subsequent offer for an interest in the property.

Market studies range in depth from level A through level D. Definitions of the various levels of analysis may be found in a variety of sources. (n 2) The next section of this paper provides a brief overview of the components of a C-level market study. The examples presented illustrate how GIS can be used in a C-level market study for various properties. Example 2. Appraisal of a Problem Property The subject property was a vacant, recently completed shopping center (see Figure 6). It was valued by two appraisers, but neither performed a proper market analysis. Each appraiser knew that the other appraiser was appraising the same property at the same time.

Both appraisers were contacted while their reports were being reviewed. One appraiser said he had asked the other appraiser to split the cost of a demographic ring study. The ring study was in one of the reports, but it was included without any meaningful comments as to the implications of the data. The data showed what this map indicates: the property was a neighborhood shopping center without a neighborhood, so to speak, because the neighborhood population was so small. Both appraisers went some distance outside the neighborhood to find rent comparable's from shopping centers that had surrounding neighborhoods. They then assumed that the subject property would lease in the standard 18-month time frame.

In fact, many appraisers simply assume that problem properties will lease in the standard period of time. More than 100 shopping centers in this metropolitan area reportedly had similar problems. Later on, the property was checked every few years and it was never more than about one-third occupied. Problems like this are readily caught with GIS.

Market area delineation using GIS is shown in the next section. If the sales potential of the property had been analyzed using demographic data and GIS, the inadequate demand would have been identified. Example 3. Probable Collusion The property in this example reportedly caused a bank to fail. The property was appraised for one bank at $46 million in light of some contamination issues. Two local appraisers appraised the property for another FDIC office and ended up with the same value conclusion of approximately $30 million, leading one to suspect collusion.

Hundreds, if not thousands, of 'paired sets' of land appraisals were reviewed over six years and none of the other pairs of appraised values were nearly as close. Account officers at the FDIC contacted more than 500 developers and potential owners / users and met with over 50 developers in attempt to sell the property at the appraised value. The property was also listed with more than 1,300 brokers. No offers came anywhere close to the appraised values. The asset was transferred to the western regional office of the FDIC and it was concluded that the values were not properly supported. A statement of work was written for two more appraisals.

(Statements of work are detailed instructions that specify the scope of services to be rendered.) The statement required that both appraisers provide C-level market studies before they appraised the property. There was still a sizable difference between their appraised values even with the benefit of market studies, due to the complexity of the issues involved. This second set of appraisals was reconciled to values that differed by less than 20%. The appraisers also estimated holding periods of about two years before the property could be sold. The property did sell about two years later and the present value of the price was very similar to the average appraised values, which amounted to approximately $7 million.

Collusion is a problem that can negate the benefit of the worthwhile practice of obtaining two current appraisals for the same asset. A review appraiser equipped with GIS would be better able to spot these problems. Problems with the prior appraisals clearly demonstrate the need for market studies for acreage. Submarket Analysis by Institutional Investors More and more institutional investors are using GIS to analyze acquisitions. Institutional investors will not rely on 'seat-of-the-pants' estimates, as other developers have, and they are becoming more prevalent.

One institutional investor, who gave a presentation at a dinner meeting of the Appraisal Institute last summer, was from Institutional Housing Partners (IHP). He said that conventional appraisal methodology is flawed because: It relies solely on historic sales, which reflect analyses that were often done much earlier than the sale date. The sales used are often of properties located outside the subject's market area. Appraisers do not attempt to forecast the demand for housing within a sub market, let alone by market segment within the subject sub market.

The speaker showed a number of time series studies that IHP analyzes as part of purchase decisions. The company reports that, as a result of this type of analysis, their return on investment is about twice as high as the average developer's. They attribute their success to the analysis of micro markets and suggest that appraisers do the same. State Street Research is a large pension fund advisor that had been part of Equitable Life. They have used 3-D GIS mapping to analyze the Boston office market.

GIS was used to forecast office rents based on location attributes within the Boston area. State Street notes that they spend millions of dollars each year for the spatial data alone, which they analyze as part of their real estate research. The National Council of Real Estate Investment Fiduciaries (NCREIF) has provided a position paper suggesting how appraisers should analyze demand. (n 3) They recommend analyzing historical relationships between demand and other variables and then forecasting the variables that create demand for the property type being appraised. This process is known as econometric modeling.

Extension packages are available for statistical modeling within a GIS. One of these packages is known as S-Plus, and the GIS / Stat package is available for $2,800. S+ Spatial Stats, another add-on that costs approximately $1,000, is used for the analysis of spatially correlated data. The use of sophisticated techniques to analyze sales leads to an important question: Are your buyers really well informed?

Trends in the market suggest that appraisers should be using GIS to analyze micro markets if they truly want to emulate the market. Submarket Analysis by the Academic Community via GIS The University of California at Los Angeles has been providing econometric forecasts of the Southern California economy for many years. UCLA demonstrated 3-D modeling of the apartment market at a university conference held in mid-1999. At the beginning of the conference, the university noted that increased computing power and lower costs have combined to enable them to do forecasting by sub market within Los Angeles. Capitalization rates would be for apartments in Los Angeles County, by sub market, by the first quarter of the year 2000.

The Long Beach area is shown at the bottom of the map and the San Fernando and San Gabriel Valleys are at the top. The coastline is located along the lower left side. According to the scale at the bottom of the map, red is used to signify the highest cap rates. As you can see, the highest cap rates are shown for central LA. Submarket with the lowest cap rates are shown in dark blue. Lower cap rates are generally expected in the coastal areas.

As we can see, dramatic differences in cap rates are estimated for areas that are not far from each other. This underscores the need to define the proper market area for selecting comparable sales. Market area delineation via GIS is shown in the next section. Using GIS to Prepare Market Studies The Appraisal Institute provides training so that appraisers can learn how to do market studies. The C-level study referenced here consists of the six steps listed below. This section of the paper will provide snapshots that demonstrate how GIS can be used in each of the six steps.

Step 1: Productivity Analysis Productivity analysis researches how a property can best fulfill human needs. It answers the question, What is it best suited for physically? Let's say that an appraisal is needed for one of the tracts that are shown within the blue boundaries on the elevation map in Figure 8. The highest elevations have been colored white; some of them have views of Catalina to the south. The brown areas are next highest in elevation and the green areas are lower. The lowest elevations are a tan color.

Digital data for these maps is free, but it takes about a day and $2,500 to $5,000 in additional software to create such a map. The blue boundaries show recent subdivision activity. If your subject property is located in one of the areas of higher elevation at the center of the map, a productivity analysis would tell you that your property is likely to compete with other properties that have similar elevations. If your property is in the flatland's to the right, it will tend to compete with others at the same elevation. If your property has a higher elevation, you could use GIS to find home prices in the nearby luxury community shown in the upper left corner of the map. Sales of homes in this community are color-coded, as they were in the Arden-Guthrie example (Figure 1), with the red dots indicating homes that sold for more than $2 million, much higher prices than those in the flatland's.

Figure 9 shows a cutaway from the elevation map. The right side of the map shows actual land use. The lighter brown areas identify undeveloped land; the light blue areas show where medium-density residential land uses are located. The areas in green are used for farming. These are areas where the lower-priced housing is being built. The red areas have been commercially developed.

Maps like these are very useful for communicating productivity and highest and best use issues. The land use map costs $1,500 for San Bernardino County and $3,500 for Los Angeles. The map in Figure 10 shows how GIS can be used to find visibility. The two lines leading from the highest elevations, shown in white, to the bottom of the map, shown in green, have red and green segments. The red segments show areas that would be obscured from the line of sight of a viewer standing on the mountaintops.

The green line segments show areas that can be seen from the observation point, including Orange County at the bottom of the map. The upper left portion of the map shows a view shed analysis. The GIS shows that an observer at the apex of the triangle looking north (towards the top of the map) can see the land that is covered with a stippled surface. When looking from this observation point, the areas that are not covered by the stippled surface cannot be seen. This type of analysis could be done for each comparable sale to show which are most productive for luxury housing with a view amenity and why location adjustments really are appropriate for varying levels of visibility. Step 2: Delineation of the Market Area The Office of Real Estate Appraisers (OREA) has recognized the importance of properly defining market area as part of US PAP compliance.

This step is crucial because if the wrong geographic area is delineated, the subsequent estimates of demand and resultant values are likely to be seriously understated or overstated. GIS can also be used to define the proper market area by travel time, incomes, employment, ethnicity, and other factors. In this example, the GIS map in Figure 11 is used to show the ethnic makeup of the population in each census block group by way of pie charts. The pie charts increase in size based on the number of people in each census block group. The map shows that there is a strong concentration of Asians living in the block groups that have been delineated by the solid black line. This map shows how ethnicity could be one way of defining a market area.

Trial market delineations like the one shown in Figure 11 can be tested using survey data from subdivisions that are currently selling out. In this way the appraiser can find the primary and secondary market areas that are likely to be the most accurate for compiling information on future demand. Step 3: Analysis of Demand NCREIF has provided appraisers with a suggested methodology to use in quantifying the future demand for a property. The first step is to identify the variable (s) that are most responsible for the demand for a given property type.

An outline of the basic growth model that explains many types of demand is shown in Figure 12. It is very similar to one shown in a textbook on market studies that is used for the C CIM designation. Basic employment is the cause of changes in office and industrial demand. The resultant change in population and income creates changes in housing and retail demand. Once the relevant market area is delineated, relationships can be observed between one variable, say employment, and the change in housing demand. Assume that we find that one new home is sold for every three new jobs that are created in the market area.

If you can forecast the number of new jobs in the market area, you can base an estimate of the future absorption of new homes on the ratio of one new home for every three new jobs. The sample calculation below shows how an estimate of the change in employment from a recent market study was used to calculate residential demand versus actual residential absorption. The model calculated a demand for 1,470 new homes based on job growth; 1,576 homes were actually absorbed in the same time period. The San Diego Association of Governments, better known as SANDAG, has been a leader in the use of GIS.

SANDAG used GIS to analyze the habitat for endangered species (n 4) and to help rebuild the local economy after aerospace and banking departed from the county earlier in the decade. The cluster analysis used by SANDAG and shown in Figure 14 is beyond the scope of this presentation. The following quote came from one of their publications: Cluster analysis is made easier when combined with a Geographic Information System (GIS). For example, combining cluster definitions, GIS software and an employment database allows analyses that were only hypothetical a few years ago. The GIS / cluster partnership opens up a myriad of possibilities for economic development organizations and could form the basis of a regional economic development information system.

Using a GIS enables the user to connect each business address and name to its cluster attribute. The Southern California Association of Governments, known as SCAG, is SANDAG's counterpart in the LA Basin. SCAG has been trying to get the cities in its region to start using GIS. One of the incentives has been the Access program, which offers cities free software and spatial databases. The author has found that a combination of input-output and cluster analysis can be a very effective method of forecasting demand. The combination is currently being tested for the redevelopment of brownfield's. (n 6) The darkest red area indicates the highest growth rates.

In this case, growth rates range from 2% to 13%. More detail is needed for market studies since we also need to look at individual segments of the population, not just totals. The pie chart shows the proportion of population growth in each census block group. Employment growth for the highest income segment cannot be used as reliably for modeling demand. This is because some demand comes from immigrants who are self-employed or don't need to work.

As a result, we have to resort to demographic projections by outside experts. The pie charts on this map show that the increase in the local population is expected to be almost exclusively Asian. The areas outside of the primary market area are expected to be mainly Hispanic. We are only interested in the demand from the most affluent segment of the population, so we have to go to very expensive data sources to obtain them. One source of data like this costs $4,000 for an annual subscription; this is not the type of free data that can be found on the Internet. Again, the important consideration for a luxury home development is the future trend in the number of households in the highest income segment.

Figure 17 shows that most of the block groups outside the market area are expected to have declines or slight increases in this segment of the population. The subject tracts we looked at in Figure 8 are at the lower right portion of Figure 17. This area is expected to be the focal point of the growth in the highest income segment -- households earning more than $100,000 per year. This is the segment that is critical for quantifying future demand for luxury homes on sites that have excellent views. Step 4: Analysis of Supply Institutional Housing Partners stressed the need for appraisers to segment residential supply and demand. GIS can be used to identify new housing developments with elevations similar to the ones we have been looking at.

In other words, you can extend your search into three dimensions with GIS. USGS data can be used to provide a much more accurate picture of the competitive supply. Digital data can be downloaded from the USGS site. A sample map is shown in Figure 18. The prices at which properties have been sold are identified by colors on the map. This type of data is known as an.

It is superior to aerial photography that can be downloaded from the Internet and, since have been optically corrected for distortions inherent in aerial photography, areas can be scaled directly from the map without distortion. Another benefit is that this digital data can be draped over digital elevation models to create a 3-D representation of the area mapped. This results in an aerial photo that can be shown and rotated in 3-D using the proper software. This software cost $2,500 at the time this paper was written. Step 5: Analysis of the Interaction of Supply and Demand Five of them were within the primary market area and three were outside of this area, at the top of the map. GIS analysis of the three properties at the top of the map could show, for example, that they don't have nearly the same potential for future demand.

This could result from the different number of households expected to be added to the $100,000-per-year-and-up household income category (the red areas in Figure 17). If bulk sales of lots were found up there, they might have to be adjusted upward for their expected slower absorption. Although physically very similar, these properties could have different economic futures. The historic rates of absorption within the market area can then be analyzed in relation to the independent variables to develop parameters for forecasting future demand.

Step 6: Forecasting the Capture Rate GIS can create some worthwhile maps depicting absorption, such as the one shown in Figure 20. Bar charts are used in this map to show the relative capture rates, or rates of absorption, for each new home development in this market area over the last four quarters. Conclusion In conclusion, white collar real estate scams would be much more difficult to write if larger amounts of relevant market data were required. Appraisers should be encouraged to provide more than three comparable's and to show the reasons why the comparable's considered the most relevant are the most relevant. Simple, unsupported opinions should not be considered adequate.

Reviewers could map their own data to find inconsistencies and reveal potential problems. Market studies should be done more often -- especially for proposed projects and the appraisal of undeveloped acreage. Appraisal fees should allow appraisers to 'show what they know,' to justify market boundaries, and to demonstrate why their comparable's really are from similar areas. It is hoped that the examples provided here are sufficient to demonstrate how useful GIS can be as a tool for accomplishing all of these goals. Although using GIS can be very expensive, an additional $5,000 to $10,000 for a market study can be readily justified to avoid potential losses that could easily run into millions of dollars. Institutional investors spend a lot of money analyzing complex markets.

It seems that appraisers should be able to do the same to truly emulate the thought processes of the buyers that they hope to understand. The banking crisis has shown that the appraisal methodologies developed in the 1930's are due for a major overhaul.

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