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We had a much larger and more comprehensive sample available to us than was employed in Hirsch (1988) or Hirsch et al (1988) or Hirsch et al (1999). For our primary analysis, we focused on 137,983 observations collected from over twenty years of mobile home transactions between 1983 and the early part of 2003. The ultimate transaction data base included only repeat- or multiple -sales along with descriptive information about the coach. Each sale was geo-coded permitting the census tract variables including Median Household Income (constant $, 1996), Proportion of Households with Public Assistance Income and Proportion of Persons > 65 Years Old to be appended to each record. The first two are proxies for local amenity values as well as demand, while the third is a proxy for one of the components of demand for mobile home units as many older households choose mobile homes as a cost effective housing choice in retirement. Again, the definitions of the descriptors of the various transactions are shown in Table 1 and the descriptive statistics of the covariates are shown in Tables 4 and 5. Because we have repeat- or multiple -sales in our data base, it is straight forward to compute the Average Annual Growth Rate (AAG) in nominal and real terms for each coach or mobile home between the date of initial purchase and the date of the subsequent sale. We then regressed the AAG in constant dollars on coach and neighborhood characteristics along with an indicator variable for the rigidity of the rent control regime. Our working hypothesis was that because rigid rent control policies allow coach owners to pass on future pad rent savings to subsequent owners of the coach, prices of coaches in those communities will increase more rapidly or decrease less rapidly than the prices of coaches in communities without rent control or flexible rent control. Thus, if rent control or the rigidity of the rent control regime influences the rate of change of coach prices, the relevant estimated coefficient will be positive and significant. The key results of cross -sectional hedonic regressions of the data for the 20 years are shown in Table 6a, 6b and 6c. We report results after having explored numerous alternative specifications and alternative approaches to segmenting the data. As will become clear, the rigidity of the rent control regime, the legal status of rent control policies (influenced by the cited U.S. Supreme Court decision in 1992), neighborhood household income and neighborhood age of household seem to impact the price effect associated with rent control policies. We report results of the analysis segmented by: