Compared to the previous twenty years, residential investments in the US appear more stable after the mid-1980s. Chapter 2 explores key hypotheses regarding the underlying causes. In particular, it uses estimated DSGE models to examine whether a more responsive interest rate policy stabilizes the housing market by keeping inflation in check. These estimations indeed found a policy that has become more responsive over time. Counter-factual analysis confirms that the change stabilizes inflation as well as nominal interest rate. It does not, however, find the change in policy to have stabilizing effect on real economic activity including housing investment. It finds that smaller TFP shocks make modest contributions, while the biggest contributing factor to the fall in the housing volatility is a reduction in the sensitivity of the investment to demand variations. Chapter 3 constructs a richly specified model for the housing market to examine the empirical relevance of various costs and frictions, including the investment adjustment cost, sticky construction costs, search frictions, and sluggish adjustment of house prices. Using the US national-level quarterly data from 1985 and 2007, we find that the gradual adjustment of house prices is the most important and irreplaceable feature of the model. The key to developing an optimization-based empirical housing model, therefore, is to provide a structural interpretation for the slow adjustment in house prices. Chapter 4 uses US national-level time series of residential investment, price index of new houses, consumption and interest rate to explore whether the US, as a nation, experienced a drop in the price elasticity of supply of new housing. Maximum likelihood estimations with a simple stock-and-flow model found a statistically significant drop of the elasticity from 10 to 2.2, when the quarterly data between 1971 and 2007 are split at 1985. A richer model with mechanisms of gradual adjustment also indicates such a reduction, when existing knowledge about the adjustment parameters is incorporated in the analysis. For the Federal Reserve, an inelastic supply can be a source of concern, because policy-driven demand in housing market is more likely to trigger undesirable swings in prices.

Compared to the previous twenty years, residential investments in the US appear more stable after the mid-1980s. Chapter 2 explores key hypotheses regarding the underlying causes. In particular, it uses estimated DSGE models to examine whether a more responsive interest rate policy stabilizes the housing market by keeping inflation in check. These estimations indeed found a policy that has become more responsive over time. Counter-factual analysis confirms that the change stabilizes inflation as well as nominal interest rate. It does not, however, find the change in policy to have stabilizing effect on real economic activity including housing investment. It finds that smaller TFP shocks make modest contributions, while the biggest contributing factor to the fall in the housing volatility is a reduction in the sensitivity of the investment to demand variations. Chapter 3 constructs a richly specified model for the housing market to examine the empirical relevance of various costs and frictions, including the investment adjustment cost, sticky construction costs, search frictions, and sluggish adjustment of house prices. Using the US national-level quarterly data from 1985 and 2007, we find that the gradual adjustment of house prices is the most important and irreplaceable feature of the model. The key to developing an optimization-based empirical housing model, therefore, is to provide a structural interpretation for the slow adjustment in house prices. Chapter 4 uses US national-level time series of residential investment, price index of new houses, consumption and interest rate to explore whether the US, as a nation, experienced a drop in the price elasticity of supply of new housing. Maximum likelihood estimations with a simple stock-and-flow model found a statistically significant drop of the elasticity from 10 to 2.2, when the quarterly data between 1971 and 2007 are split at 1985. A richer model with mechanisms of gradual adjustment also indicates such a reduction, when existing knowledge about the adjustment parameters is incorporated in the analysis. For the Federal Reserve, an inelastic supply can be a source of concern, because policy-driven demand in housing market is more likely to trigger undesirable swings in prices.

Compared to the previous twenty years, residential investments in the US appear more stable after the mid-1980s. Chapter 2 explores key hypotheses regarding the underlying causes. In particular, it uses estimated DSGE models to examine whether a more responsive interest rate policy stabilizes the housing market by keeping inflation in check. These estimations indeed found a policy that has become more responsive over time. Counter-factual analysis confirms that the change stabilizes inflation as well as nominal interest rate. It does not, however, find the change in policy to have stabilizing effect on real economic activity including housing investment. It finds that smaller TFP shocks make modest contributions, while the biggest contributing factor to the fall in the housing volatility is a reduction in the sensitivity of the investment to demand variations. Chapter 3 constructs a richly specified model for the housing market to examine the empirical relevance of various costs and frictions, including the investment adjustment cost, sticky construction costs, search frictions, and sluggish adjustment of house prices. Using the US national-level quarterly data from 1985 and 2007, we find that the gradual adjustment of house prices is the most important and irreplaceable feature of the model. The key to developing an optimization-based empirical housing model, therefore, is to provide a structural interpretation for the slow adjustment in house prices. Chapter 4 uses US national-level time series of residential investment, price index of new houses, consumption and interest rate to explore whether the US, as a nation, experienced a drop in the price elasticity of supply of new housing. Maximum likelihood estimations with a simple stock-and-flow model found a statistically significant drop of the elasticity from 10 to 2.2, when the quarterly data between 1971 and 2007 are split at 1985. A richer model with mechanisms of gradual adjustment also indicates such a reduction, when existing knowledge about the adjustment parameters is incorporated in the analysis. For the Federal Reserve, an inelastic supply can be a source of concern, because policy-driven demand in housing market is more likely to trigger undesirable swings in prices. / Arts, Faculty of / Vancouver School of Economics / Graduate

<p>In this thesis I study the German housing market and specifically the level of housing investment. First, a theoretical background to housing market dynamics is presented and then I test whether there is a relationship between housing investments and GDP, the size of the population, Tobin’s Q and construction costs. An Error Correction Model is estimated and the result is that the equilibrium level of housing investment is restored after less then two quarters after a change in one of the explainable variables. The estimation indicates that GDP, the size of the population and construction costs affect the level of construction in the short run. However, in the long run the only significant effect is changes in construction cost.</p>

In this thesis I study the German housing market and specifically the level of housing investment. First, a theoretical background to housing market dynamics is presented and then I test whether there is a relationship between housing investments and GDP, the size of the population, Tobin’s Q and construction costs. An Error Correction Model is estimated and the result is that the equilibrium level of housing investment is restored after less then two quarters after a change in one of the explainable variables. The estimation indicates that GDP, the size of the population and construction costs affect the level of construction in the short run. However, in the long run the only significant effect is changes in construction cost.

Housing investment plays a vital role in the real estate market. Although the housing investment has been extensively investigated, the application of Tobin¡¦s Q theory is relatively minor. Hence, the purpose of this study is to apply Tobin¡¦s Q theory to analyze housing investment, using quarterly data for Taipei City from 1973 Q2 to 2010 Q4. The Q ratio numerator is the pre-sale housing price and the denominator represents the value of the rent. The empirical model is estimated by using building permits and use permits as measures of housing investment. Moreover, because the housing market is imperfect, this study applies the threshold regression model to test whether different effects exist in the Q ratio. Finally, this study also compares housing investment in five cities. In conclusion, these findings imply that the Q ratio has a positive relationship with housing investment, as well as a threshold effect. Furthermore, the local housing investments are affected differently by local variables.

This thesis investigates the relationship between Tobin’s Q and regional housing investment in Sweden for the time period of 1998-2012. The relationship is tested through estimation of two models for time-series analysis, a vector error correction model (VECM) and an autoregressive distributed lag (ARDL) model. Depending on which model that is used, I find some evidence of positive correlation between Tobin’s Q and regional housing investment in the long run while the short run dynamics of investment does not seem to be explained by Tobin’s Q. By transforming the regional data into a panel data set and running a fixed effects model, I examine the gain in explanatory power of Tobin’s Q from using disaggregated data rather than aggregated. My findings suggest that using disaggregated data improves the explanatory power of Tobin’s Q on investment. However, the Granger Causality test indicates two-way causality between Tobin’s Q and investment, causing endogeneity problem in the estimated equations.

In chapter one of the thesis, we incorporate shocks to the efficiency with which firms learn from production activity and accumulate knowledge into an otherwise standard real DSGE model with imperfect competition. Using real aggregate data and Bayesian inference techniques, we find that learning efficiency shocks are an important source of observed variation in the growth rate of aggregate output, investment, consumption and especially hours worked in post-war US data. The estimated shock processes suggest much less exogenous variation in preferences and total factor productivity are needed by our model to account for the joint dynamics of consumption and hours. This occurs because learning efficiency shocks induce shifts in labour demand uncorrelated with current TFP, a role usually played by preference shocks which shift labour supply. At the same time, knowledge capital acts like an endogenous source of productivity variation in the model. Measures of model fit prefer the specification with learning efficiency shocks. The results are robust to the addition of many observables and shocks. In chapter 2, I estimate a "Learning-by-doing'' model with "Learning efficiency shocks'' using Bayesian estimation techniques and real aggregate data from Euro Area. I find that learning efficiency shocks explain a large fraction of the fluctuations in the growth rate of real aggregate variables such as consumption, output, investment and employment. This paper is the first to estimate a learning-by-doing model with learning efficiency shocks for the Euro Area and analyses its business cycles. In chapter 3, We study the impact of COVID 19 pandemic on the Canadian housing market. The Canadian economy has been hit hard by the COVID-19 pandemic like almost every other country in the World. The residential real estate market that makes a significant contribution to the Canadian economy however behaved far differently in the wake of the COVID-19 downturn. Unlike previous recessions, housing market recovered much faster and house prices steadily increased from 2020:QII. Since the pandemic has started, working from home (WFH) has become more prevalent. How important is WFH in producing large swings in house prices as observed in the data? To address this question, we estimate an augmented New Keynesian model with collateralized household debt and remote working condition. We argue that remote working condition improves the performance of the model, particularly explaining the house price dynamics in the last two years. / Thesis / Doctor of Philosophy (PhD)