Magazine article Real Estate Issues

Improving Real Estate Market Research

Magazine article Real Estate Issues

Improving Real Estate Market Research

Article excerpt

Real estate market research is a broad term by definition. To the appraiser market research is to identify appropriate comparables for valuation purposes. To the real estate counselor, real estate market research is to be concerned with demand, supply and price or rental rates. To the academic researcher real estate market research is quantitative analysis of demand and supply factors which culminate in an econometric model of the market being studied.

Because of the numerous connotations of the term real estate market research, for purposes of this article it is defined as the study of the economic structure and performance of real estate markets, including the development of theoretical and empirical frameworks or models that facilitate the understanding of how markets work as total systems. To accomplish this, one must understand demand and supply fluctuations and how they jointly determine price and rental rates and the driving forces behind demand and supply and how these forces have behaved historically.

To this definition, we can add that the goals of real estate market research are to understand how markets react to changes in exogenous variables and to forecast market movements with a reasonable degree of success. Understanding market reactions and forecasting likely future movements are the raison d'etre of real estate market research.


We can classify empirical models of real estate markets into two basic types: econometric and judgmental. Real estate market research requires the development and application of both types of models.


Econometric models can be powerful analytical tools, but they are extremely data hungry. To develop a properly specified econometric model, one must have a sufficient amount of high-quality data. However, the availability of data is a primary binding constraint to the real estate market researcher. Since most real estate is privately owned, information about its performance is difficult to obtain. In research oriented toward evaluating the performance of properties for investment purposes, a classic data problem is that of appraisal-based versus market transaction measures of performance. Problems such as this often are compounded by limited time series or cross-section sample sizes, which call for special approaches, such as Bayesian techniques.

Of course, there is danger in building ad hoc econometric models that do not have adequate theory behind them. Lack of adequate data can cause variables to be misstated or omitted from ad hoc econometric models. Misstated or omitted variables can reduce the value of econometric models in helping to understand and forecast real estate markets. It also is difficult to forecast with econometric models because relationships among variables may change due to fundamental factors that have not been captured fully in the model.

Many econometric models of real estate markets do not employ simultaneous equations that incorporate a price or rent variable because accurate price or rent data are difficult to obtain in sufficient quantity and quality. Despite these pitfalls, econometric models can be useful for quantifying relationships among variables, e.g., office space absorption as a function of employment, population, etc.


The judgmental model is a less elegant but nonetheless useful alternative to the econometric model. The term judgmental has been applied to the class of models that are based on the analysts judgment of the quantitative relationship among variables rather than on statistical estimation of relationships. Most often, judgmental models operate within a spreadsheet environment.

For example, the office judgmental model is a simple, step-by-step approach for translating employment forecasts into forecasts for the demand for office space based on employment and space parameters. A judgmental model of demand for office space usually begins with a forecast for employment by industry. …

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