1. Housing Demand in Germany and Japan Borsch-Supan, Heiss, and Seko, JHE 10, 229-252 (2001) Presented by Mark L. Trueman, 11/25/02

2. Commonalities Market economies with essentially private housing markets Strong government intervention in the form of housing subsidies/ market regulations Comparable standards of living Comparable demographics…e.g. high elderly share “Americanized”- similar consumption patterns

3. Differences

4. Problem/Approach Problem: –Typically, we draw inferences about components of demand by looking at differences and similarities across HH. Such cross-sectional data in a single country features little price variations. Goal: Exploit cross-national variation: 1.Identify determinants of housing demand 2.Separate differences in preference parameters from HH attributes & socio-economic characteristics Econometric method: –A flexible discrete choice model is used- Mixed Multinomial Logit Model (MMLM)

5. Methodology Since housing is a durable good, use “permanent (or normal) income” when est. demand. Since housing is a heterogeneous good, normalize quality and quantity (i.e., find a well-defined, standard dwelling). Use classical hedonic approach. Model housing demand as a multidimensional choice- major attributes are tenure, size, structure type

6. Prior Econometric Specifications The basic MNL models derive choice probabilities and a likelihood function. Mistaken assumption is that the error components which carry all unobserved heterogeneity are independent of each other. Problem: seriously biased parameter estimates of housing demand determinants.

7. Enhanced Model Specification Model housing demand as a dynamic, decision making process using a mixed multinomial logit (MMNL) model. Model allows for: –a flexible substitution pattern among alternatives. –unobserved heterogeneity in panel data

8. Model Summary Housing demand equations: joint-dependent variables are the probabilities of the 8 housing alternatives, explained by housing prices, HH income, set of socio-econ characteristics (age of HH, HH size; both quadratic forms) Plus, a linear time trend Alternative specific constants-interaction of the other variables with the 3 dimensions-tenure, type, size.

9. Structure/ Results Goodness of Fit- MMNL model provides a much better fit than basic MNL model

10. Interpreting Elasticities from Significant Coefficients Coefficients in discrete choice models carry little intuitive information. They must be converted to be meaningful. Effects are roughly symmetrical.

11. More!

12. Conclusions Key: pooling data from different countries offers more variation in explanatory variables. Result: we get better estimates of the income, age, and size effects (elasticities).