Intercity Rent Differentials in the U.S. Housing Market 2000: Understanding Rent Variations as a Sociological Phenomenon

ABSTRACT:

This study extends the intercity rent differentials investigation by Gilderbloom and Appelbaum (1988) in relatively independent housing markets to see how it can be replicated using U.S. census data from the year 2000 against the 1970 and 1980 models with the addition of several new variables to measure its impact on intercity rents. We find that region, race, and climate no longer explain rent differentials in 2000 as it did in the 1980 research, while affirming that a large percentage of old houses and small mom-and-pop landlords causes rents to fall. We find that both the cost of homeownership and the level of household income remain critical factors in explaining the level of median rent across cities. We also find a strong correlation between cities with extensive anti-war activity in the late 1960s and same sex households having higher rents, although more research needs to be done before we argue a causal relationship. We contend that sociology needs to be put back into the equation in order to understand how rents vary from city to city. Our explanation of rent variations adds a social dimension that most other researches miss. We also show how the amount of explanatory power is increased significantly by adding in a sociological dimension.

Notes

1 After consulting the Kentucky State Data Center (KYSDC), there are no data on the square footage of cities nor broken down by houses on the average. The 2000 Census questionnaire revealed no questions were asked on housing unit size or city size in square feet. Also, Summary File 3 of 2000 census revealed that no such data exist. A demographer at the KYSDC stated that some square footage data are available on the American Housing Survey (AHS) of the U.S. Census Bureau; however, this is done for a sample of cities (not a 100% count), and the cities in the sample change from survey point to survey point. The only other way to acquire complete data on square footage of cities and for housing units would be to individually contact city tax assessors and we simply don’t have the resources to do that. Even if the data were found, we don’t think the regression coefficients would have been changed in any significant manner.