The 1998 ?2005 Housing "Bubble" and the Current "Correction": What’s Different This Time?

This paper examines the inflation in housing prices between 1998 and 2005 and investigates whether this run-up in prices can be ‘‘explained’’ by increases in demand fundamentals such as population, income growth, and the decline in interest rates over this period. Time series models are estimated for 59 MSA markets and price changes from 1998 to 2005 are dynamically forecast using actual economic fundamentals to drive the models. In all 59 markets, the growth in fundamentals from 1998 to 2005 forecasts price growth that is far below that which actually occurred. An examination of the 2005 forecast errors reveals they are greater in larger MSAs, in MSAs where second home and speculative buying was prevalent, and in MSAs where indicators suggest the sub-prime mortgage market was most active. These latter factors are unique to the recent housing market and hence make it difficult to asses if and how far housing prices will ‘‘correct’’ after 2005.

What will it take to restore the housing market?

March 1, 200

Does the incentives structure matter for the design of large-scale privatization in Ukraine?

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1995.Includes bibliographical references (leaves 51-54).by Gleb L. Nechayev.M.C.P

Error Correction Models of MSA Housing "Supply" Elasticities: Implications for Price Recovery

MSA-level estimates of a housing supply schedule must offer a solution to the twin problems of simultaneity and stationarity that plague the time series data for local housing prices and stock. An Error Correction Model (ECM) is shown to provide a solution to stationarity, but not simultaneity. A Vector Error Correction Model (VECM) is suggested to handle both the stationarity and endogeneity problems. Such models also nicely distinguish between (very) long run elasticities and a variety of short term impacts. We estimate these models separately for 68 US MSA using quarterly data on housing prices and residential construction permits since 1980. The results provide long run supply elasticity estimates for each market that are better bounded than previous panel-based attempts and also correspond with much conventional thought. We find these elasticities are well explained by geographic and regulatory barriers, and that inelastic markets exhibit greater price volatility over the last two decades. Using the models’ short run dynamics we make several forecasts of prices over the next decade. In current dollars, some MSA will still not recover to recent peak (2007) house price levels by 2022, while others should exceed it by as much as 70%.The authors are indebted to the MIT Center for Real Estate, and CBRE. They remain respnosible for all results and conclusions derived there from