The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Stefano Neri - One of the best experts on this subject based on the ideXlab platform.

  • Housing market spillovers evidence from an estimated dsge model
    American Economic Journal: Macroeconomics, 2010
    Co-Authors: Matteo Iacoviello, Stefano Neri
    Abstract:

    We study sources and consequences of fluctuations in the US Housing market. Slow technological progress in the Housing sector explains the upward trend in real Housing prices of the last 40 years. Over the business cycle, Housing Demand and Housing technology shocks explain one-quarter each of the volatility of Housing investment and Housing prices. Monetary factors explain less than 20 percent, but have played a bigger role in the Housing cycle at the turn of the century. We show that the Housing market spillovers are nonnegligible, concentrated on consumption rather than business investment, and have become more important over time.

  • Housing market spillovers evidence from an estimated dsge model
    American Economic Journal: Macroeconomics, 2010
    Co-Authors: Matteo Iacoviello, Stefano Neri
    Abstract:

    The ability of a two-sector model to quantify the contribution of the Housing market to business fluctuations is investigated using U.S. data and Bayesian methods. The estimated model, which contains nominal and real rigidities and collateral constraints, displays the following features: first, a large fraction of the upward trend in real Housing prices over the last 40 years can be accounted for by slow technological progress in the Housing sector; second, residential investment and Housing prices are very sensitive to monetary policy and Housing Demand shocks; third, the wealth effects from Housing on consumption are positive and significant, and have become more important over time. The structural nature of the model allows identifying and quantifying the sources of fluctuations in house prices and residential investment and measuring the contribution of Housing booms and busts to business cycles.

Bo Malmberg - One of the best experts on this subject based on the ideXlab platform.

  • demography and Housing Demand what can we learn from residential construction data
    Journal of Population Economics, 2008
    Co-Authors: Thomas Lindh, Bo Malmberg
    Abstract:

    There are obvious reasons why residential construction should depend on the population’s age structure. We estimate this relation on Swedish time series data and Organization for Economic Cooperation and Development panel data. Large groups of young adults are associated with higher rates of residential construction, but there is also a significant negative effect from those above 75. Age effects on residential investment are robust and forecast well out-of-sample in contrast to the corresponding house price results. This may explain why the debate around house prices and demography has been rather inconclusive. Rapidly aging populations in the industrialized world makes the future look bleak for the construction industry.

  • demography and Housing Demand what can we learn from residential construction data
    Research Papers in Economics, 2005
    Co-Authors: Thomas Lindh, Bo Malmberg
    Abstract:

    There are obvious reasons why residential construction should depend on the population’s age structure. We estimate this relation on Swedish time series data and OECD panel data. Large groups of young adults are associated with higher rates of residential construction. But there is also a significant negative effect from those above 75. Age effects on residential investment are robust and forecast well out-of-sample in contrast to the corresponding house price results. This may explain why the debate around house prices and demography has been rather inconclusive. Rapidly aging populations in the industrialized world makes the future look bleak for the construction industry of these countries.

Tao Zha - One of the best experts on this subject based on the ideXlab platform.

  • a theory of Housing Demand shocks
    Research Papers in Economics, 2019
    Co-Authors: Zheng Liu, Pengfei Wang, Tao Zha
    Abstract:

    Aggregate Housing Demand shocks are an important source of house price fluctuations in the standard macroeconomic models, and through the collateral channel, they drive macroeconomic fluctuations. These reduced-form shocks, however, fail to generate a highly volatile price-to-rent ratio that comoves with the house price observed in the data (the ?price-rent puzzle?). We build a tractable heterogeneous-agent model that provides a microeconomic foundation for Housing Demand shocks. The model predicts that a credit supply shock can generate large comovements between the house price and the price-to-rent ratio. We provide empirical evidence from cross-country and cross-MSA data to support this theoretical prediction.

  • a theory of Housing Demand shocks
    Social Science Research Network, 2019
    Co-Authors: Zheng Liu, Pengfei Wang, Tao Zha
    Abstract:

    Aggregate Housing Demand shocks are an important source of house price fluctuations in the standard macroeconomic models, and through the collateral channel, they drive macroeconomic fluctuations. These reduced-form shocks, however, fail to generate a highly volatile price-to-rent ratio that comoves with the house price observed in the data (the “price-rent puzzle”). We build a tractable heterogeneous-agent model that provides a microeconomic foundation for Housing Demand shocks. The model predicts that a credit supply shock can generate large comovements between the house price and the price-to-rent ratio. We provide empirical evidence from cross-country and cross-MSA data to support this theoretical prediction. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

  • a theory of Housing Demand shocks
    National Bureau of Economic Research, 2019
    Co-Authors: Zheng Liu, Pengfei Wang, Tao Zha
    Abstract:

    Housing Demand shocks are an important source of Housing price fluctuations and, through the collateral channel, they drive macroeconomic fluctuations as well. However, these reduced-form shocks in the standard macro models fail to generate the observed large fluctuations in the Housing price-to-rent ratio. We build a tractable heterogeneous-agent model that provides a microeconomic foundation for Housing Demand shocks. Households with high marginal utility of Housing face binding credit constraints, giving rise to a liquidity premium in the aggregated Housing Euler equation. The liquidity premium drives a wedge between the house price and the average rent and allows credit supply shocks to generate large fluctuations in house prices and the price-to-rent ratio.

Matteo Iacoviello - One of the best experts on this subject based on the ideXlab platform.

  • Housing market spillovers evidence from an estimated dsge model
    American Economic Journal: Macroeconomics, 2010
    Co-Authors: Matteo Iacoviello, Stefano Neri
    Abstract:

    We study sources and consequences of fluctuations in the US Housing market. Slow technological progress in the Housing sector explains the upward trend in real Housing prices of the last 40 years. Over the business cycle, Housing Demand and Housing technology shocks explain one-quarter each of the volatility of Housing investment and Housing prices. Monetary factors explain less than 20 percent, but have played a bigger role in the Housing cycle at the turn of the century. We show that the Housing market spillovers are nonnegligible, concentrated on consumption rather than business investment, and have become more important over time.

  • Housing market spillovers evidence from an estimated dsge model
    American Economic Journal: Macroeconomics, 2010
    Co-Authors: Matteo Iacoviello, Stefano Neri
    Abstract:

    The ability of a two-sector model to quantify the contribution of the Housing market to business fluctuations is investigated using U.S. data and Bayesian methods. The estimated model, which contains nominal and real rigidities and collateral constraints, displays the following features: first, a large fraction of the upward trend in real Housing prices over the last 40 years can be accounted for by slow technological progress in the Housing sector; second, residential investment and Housing prices are very sensitive to monetary policy and Housing Demand shocks; third, the wealth effects from Housing on consumption are positive and significant, and have become more important over time. The structural nature of the model allows identifying and quantifying the sources of fluctuations in house prices and residential investment and measuring the contribution of Housing booms and busts to business cycles.

Mark T Hon - One of the best experts on this subject based on the ideXlab platform.

  • a cross section analysis of the income elasticity of Housing Demand in spain is there a real estate bubble
    Social Science Research Network, 2005
    Co-Authors: Daniel Fernandezkranz, Mark T Hon
    Abstract:

    Much attention has been given to claims that real estate prices in Spain are overvalued in relation to income and how plummeting house prices can jeopardize the economy (The Economist, 2003 and IMF, 2004). The measure of income elasticity on Housing expenditure is often of considerable interest to applied researchers and policy makers in real estate economics, but the problem of omitted variables in some estimation techniques can lead to severe biases. In this paper we estimate the income elasticity of the Demand for Housing in Spain based on the cross-section of prices and income in fifty Spanish provinces from 1996 to 2002. In comparison to long-run equilibrium models fitted with time-series data, our results show a much weaker role of income growth as a vehicle for house price increases in the long-run. According to our estimates, the rate of growth of house prices in Spain between 1998 and 2003 points to a real estate bubble with prices above the long-term equilibrium level.