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Property Taxes in Indiana
In 20 03 new housin g in vestment in L ake Count y f ell about 0. 25% below th e state’ s
average of 1.03%. It was argued at the time that this shortfall is due to the sharp increase in the
tax burden i mposed on h omeowners. Indeed, pr operty tax rat es in Lake Count y were (and
remain) higher than the state’s average and new housing investment was lower. However, the
coefficient for the property tax variable is actually positive and significant. Likely this means that
prospective homebuilders view the higher rate as indicative of public services offered in the
county. Unless we include controls for the services offered, interpretation of this result is unclear,
if not useless.
Using the coefficients in Table 2 an d basic data regarding Lake County vs. other
counties, it is actually Lake County’s higher unemployment rate and higher unit construction cost
that explain the vast m ajority of this shortfall and actually ove rshoots the difference. Lake
County’s unemployment rate was 1.2% higher than the rest of the state. Given the coefficient for
the UE variable, this translates into roughly a .20% shortfall in new investment, or about 80% of
the 0.25% deficit. Also, Lake Count y’s significantly higher unit construction costs account for
0.22%. It is important to remember we have not controlled for the size of the homes being built
or land value s. Lake Count y’s slightly higher median income, younger p opulation profile and
higher median home value work to partially counteract the unem ployment and cost effects. In
sum, Lake Count y’s unemployment rate and cons truction costs are enough to m ore than explain
the short-fall in new investment.
Ideally, to m ore full y understand the re lationship b etween property tax rates and new
housing investment additional data needs to be included in the study. As mentioned above, it will
be necessary to include controls for the quality and mix of public services purch ased by property
taxes. Proxies might include county expenditures, school performance measures, crime statistics,
and the li ke. As well, it would be ideal if land prices and new housing characteristi cs could be
included.
Different estimation techniques may be advantageous, too. The variables comprising our
simple model presented in Table 2 were chosen in a somewhat ad hoc fashion. While not always
favored in the econo mic l iterature, both stepwise regression or factor analy sis offer alte rnative
approaches to the problem of significant correlation among explana tory variables.
Finally, the proxim ity of Lake Count y to Chi cago and th e rather high cost of
homeownership in Illinois, account ing for liv ing costs, econom ic act ivity and r elated
characteristics of neighboring areas would add rigo r. Given the rapid housing growth in the
counties neighborin g India napolis’ Mari on Count y – but not Marion itself – may indicate that
suburbanization is an important component.
REFERENCES
deLeeuw, F. and Ekanem , N. F. (19 73). “The Demand for Ho using – A Revi ew of the Cross-
Section Evidence.” Review of Economics and Statistics, Volume 53, Number 1, 1 – 10.
Himmelberg, C., Mayer, C. and Sinai, T. (2005). “Assessing High Housing Prices: Bubbles,
Fundamentals, and Misperceptions.” The Journal of Economic Perspectives, V olume
19, Number 4, 67 – 92.
Lee, T. H. and Kong, C. M. (1977). “Elasticities of Housing Demand.” Southern Economic
Journal, Volume 43, 298 – 305.
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