Sales comparison approach

From Wikipedia, the free encyclopedia
Jump to: navigation, search

The sales comparison approach (SCA) is one of the three major groupings of valuation methods, called the three approaches to value, commonly used in real estate appraisal. This approach compares a subject property's characteristics with those of comparable properties which have recently sold in similar transactions. The process uses one of several techniques to adjust the prices of the comparable transactions according to the presence, absence, or degree of characteristics which influence value. As such, all sales comparison approach methods are variations on hedonic-type measurements, which determine the value of something as the sum of the value of the various components which contribute utility.

Units of Comparison[edit]

The SCA relies on the assumption that a matrix of attributes or significant features of a property drive its value. For examples, in the case of a single family residence, such attributes might be floor area, views, location, number of bathrooms, lot size, age of the property and condition of property.

Economic Basis[edit]

The sales comparison approach is based upon the principles of supply and demand, as well as upon the principle of substitution. Supply and demand indicates value through typical market behavior of both buyers and sellers. Substitution indicates that a purchaser would not purchase an improved property for any value higher than it could be replaced for on a site with equivalent utility, assuming no undue delays in construction.

Examples of Methods[edit]

In practice, the most common SCA method used by estate agents and real estate appraisers is the sales adjustment grid. It uses a small number of recently sold properties in the immediate vicinity of the subject property to estimate the value of its attributes. Adjustments to the comparables may be determined by trend analysis, matched-pairs analysis, or simple surveys of the market.

More advanced researchers and appraisers commonly employ statistical techniques based on multiple regression methods which generally compare a larger number of more geographically dispersed property transactions to determine the significance and magnitude of the impact of different attributes on property value. Research has shown that the sales adjustment grid and the multiple regression model are theoretically the same, with the former applying more heuristic methods and the latter using statistical techniques.[1]

Spatial auto regression plagues these statistical techniques, since high priced properties tend to cluster together and therefore one property price is not independent of its neighbor. Given property inflation and price cycles, both comparison techniques can become unreliable if the time interval between transactions sampled is excessive. The other factor undermining a simplistic use of the SCA is the evolving nature of city neighborhoods, though in reality urban evolution occurs gradually enough to minimize its impact on this approach to value.

In more complex situations, such as litigation or contaminated property appraisal, appraisers develop SCA adjustments using widely accepted advanced techniques, such as repeat sales models (to measure house price appreciation over time), survey research (e.g. -- contingent valuation), case studies (to develop adjustments in complex situations) or other statistically based techniques.[2]

Further reading[edit]

  • The Appraisal of Real Estate, 12th Edition, by the Appraisal Institute is an industry-recognized textbook.
  • The Uniform Standards of Professional Appraisal Practice, by The Appraisal Foundation, updated and published annually through the 2006 edition; henceforth, updated editions are to appear biannually.
  • Sales Comparison Approach, by Lloyd D. Hanford, Jr., MAI

References[edit]

  1. ^ see, for example, Lentz and Wang, Journal of Real Estate Research, 1997
  2. ^ see, for example, Kilpatrick, Throupe, Mundy, & Spiess, Valuation of Impaired Property, When Bad Things Happen to Good Property, Robert Simons, ed., 2006