The Sensitivity Analysis of the FHA Technique of Housing Market Analysis: The Effect of Ratios and Variables, and Their Perturbations on Family and Elderly Demand Estimates

Article excerpt


In their celebrated article, Klein and Kosobud (1961, 178) had pioneered the use of both "fundamental parameters" and "simple ratios" in economic theory. Housing economists had been following the FHA techniques by focusing on such parameters and ratios in their housing demand models for a long time. We find that both these studies emphasized caution in the use of ratios. In the former, the authors stated that "... stability or plainly systematic variation in ratios must be found in order to enhance their usefulness." In the latter, the guide warned that while participation rates based on labor force or employment data "... are both useful in estimating and projecting population they have some shortcomings ... primarily from the unavailability and limitations of pertinent locality data and from the adjustments that may be required to reflect the level and trends of unemployment" (FHA 1970, 33).

As an example of the use of ratios, the FHA guide, most likely, has outdone any other framework in optimizing the number of ratios to be used in a housing analysis. Besides the participation rates, it prescribed the use of household sizes, institutional populations, demolition rates, and the rate of construction for the single and multi-family units. The guide requires inputs for at least two census years, a current period, and a forecast period, and it uses long-term natural growth rates for population, households, labor force, employment, and construction permits. These rates are kept at a high level, perhaps with an eye for simulating "golden" growth scenarios where a combination of variables would move simultaneously in the same trend and cycle. At a local level, it is apparent that market analysts would be time-constrained to perform "stability" and "variations" studies for these parameters. Hence they would be prevailed upon to accept these studies on faith as uncontroversial stable ratios and parameters. A better way for practitioners in the field to overcome such problems would be to use a less time-consuming method that could enhance their model's estimation and predictive performance. This paper provides one such method, namely, the simulation of stable parameters and normed variables within the FHA model for family and elderly market analysis.

The Models

The FHA guide subdivides housing markets into family, elderly, and military, in terms of their relative price, rent, size and other measurements. Both the family and the elderly markets have a rental component. Traditionally, the family market has rentals for both, the family and the independent living adult population between the ages of 45 to 62. The elderly rental component, which we presume to target (persons 62 years of age and over) are mostly housed in either independent living, Retirement Service Centers (RSCs), Assisted Living (ALF), Board and Care (BC), Nursing Homes (NH), and Alzheimer's (ALZ) facilities. The population housed in RSCs is characterized by an "at risk" criterion, i.e., persons 62 and over who can perform less than three activities of daily living (ADLs) or instrumental activities of daily living (IADLs) with difficulty. The population housed in the ALFs, BCs, NHs, and ALZs are primarily 62 years of age and over who have varying levels of care dependency, who can perform three or more ADLs or IADLs with difficulty. (1)

A major premise that we investigate is that while the FHA technique had a long gestation period, it has not evolved statistically. For instance, the family market is based on the premise that either rising labor force or an increase in population translates quantitatively into household growth, which in turn is a measure of demand for the new housing units. The quantity is then put into an income growth stream to estimate the effective demand. (2) For the elderly, we may rely on the living arrangements of the one-person households for the demand estimates, and use a survival methodology to age that population. …