Housing Markets, Regulations and Monetary Policy

Sun, Xiaojin; Tsang, Kwok Ping

doi:10.2139/ssrn.2398490

Cited by 7 publications

(7 citation statements)

References 23 publications

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“…() argue that international capital flows played a small role in driving the last house market bubble, and the key causal factor was instead the cheap supply of credit due to financial market liberalization. Recently, Sun and Tsang () focus on the inference issues when studying sources to the housing market volatility. Our paper contributes to this growing literature that seeks to understand the fundamental driving forces of the housing market and its relationship to the aggregate economy.…”

Section: Summary Statistics Of Housing Markets Price Changesmentioning

confidence: 99%

Understanding Housing Market Volatility

Fairchild¹,

Ma²,

Wu³

2015

J of Money Credit Banking

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The Campbell-Shiller present value formula implies a factor structure for the price-rent ratio of housing market. Using a dynamic factor model, we decompose the price-rent ratios of 23 major housing markets into a national factor and independent local factors, and we link these factors to the economic fundamentals of the housing markets. We find that a large fraction of housing market volatility is local and that the national factor has become more important than local factors in driving housing market volatility since 1999, consistent with the findings in Del Negro and Otrok (2007). The local volatilities mostly are due to time variations of idiosyncratic housing market risk premia, not local growth. At the aggregate level, the growth and interest rate factors jointly account for less than half of the total variation in the price-rent ratio. The rest is due to the aggregate housing market risk premium and a pricing error. We find evidence that the pricing error is related to money illusion, especially at the onset of the recent housing market bubble. The rapid rise in housing prices prior to the 2008 financial crisis was accompanied by both a large increase in the pricing error and a large decrease in the housing market risk premium.JEL codes: C32, G12, R31 Keywords: dynamic factor model, housing market, price-rent ratio, risk premium, money illusion.HOUSING MARKETS ARE PARTIALLY segmented. There does not exist a centralized market for housing assets. Demographic changes, household preferences for geographic locations, and climate plus inelastic land supply can lead We would like to thank the editor, Professor Pok-sang Lam, and two anonymous referees for insightful comments and suggestions. We also thank

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Section: Summary Statistics Of Housing Markets Price Changesmentioning

confidence: 99%

Understanding Housing Market Volatility

Fairchild¹,

Ma²,

Wu³

2015

J of Money Credit Banking

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“…The fundamental component F t in (2) is a function of the future rent growth ∆r t+1+k and the future log gross return to housing γ t+1+k . It is standard in the literature (e.g., Campbell et al 2009;Sun and Tsang 2013;Shi 2017) to assume that the log gross return to housing equals the sum of the real interest rate (i t ) and a time-varying risk premium (ϕ t ), i.e.,…”

Section: Bubble Definitionmentioning

confidence: 99%

“…The fundamental component

F_{t}

in () is a function of the future rent growth

{\Delta r_{t+1+k}}

and the future log gross return to housing

{\gamma _{t+1+k}}

. It is standard in the literature (e.g., Campbell et al., 2009; Shi, 2017; Sun & Tsang, 2013) to assume that the log gross return to housing equals the sum of the real interest rate (

i_{t}

) and a time‐varying risk premium (

\varphi _{t}

), that is,

\begin{equation} \gamma _{t}=i_{t}+\varphi _{t}. \end{equation}

The risk premium is further assumed to take the simple form

\begin{equation} \varphi _{t}=\varphi +\varepsilon _{t}, \end{equation}

where

\varphi

is the long‐run risk premium and the zero mean error term

\varepsilon _{t}

captures short‐term fluctuations brought by either market fundamentals or bubbles (Shi, 2017; Shi et al., 2020).…”

Section: Bubble Definitionmentioning

confidence: 99%

Diagnosing housing fever with an econometric thermometer

Shi

Phillips

2021

Journal of Economic Surveys

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Housing fever is a popular term to describe an overheated housing market or housing price bubble. Like other financial asset bubbles, housing fever can inflict harm on the real economy, as indeed the US housing bubble did in the period following 2006 leading up to the general financial crisis and great recession. One contribution that econometricians can make to minimize the harm created by a housing bubble is to provide a quantitative 'thermometer' for diagnosing ongoing housing fever. Early diagnosis can enable prompt and effective policy action that reduces long term damage to the real economy. This paper provides a selective review of the relevant literature on econometric methods for identifying housing bubbles together with some new methods of research and an empirical application. We first present a technical definition of a housing bubble that facilitates empirical work and discuss significant difficulties encountered in practical work and the solutions that have been proposed in the past literature. A major challenge in all econometric identification procedures is to assess prices in relation to fundamentals, which requires measurement of fundamentals. One solution to address this challenge is to estimate the fundamental component from an underlying structural relationship involving measurable variables. A second aim of the paper is to improve the estimation accuracy of fundamentals by means of an easy-to-implement reduced-form approach. Since many of the relevant variables that determine fundamentals are nonstationary and interdependent we use the IVX (Phillips et al. (2009); Kostakis et al. ( 2015)) method to estimate the reduced-form model to reduce the finite sample bias which arises from highly persistent regressors and endogeneity. The recursive evolving test of Phillips, Shi, and Yu (2015a, PSY) is applied to the estimated non-fundamental component for the identification of speculative bubbles. The new bubble test developed here is referred to as PSY-IVX. An empirical application to the eight Australian capital city housing markets over the period 1999 to 2017 shows that bubble testing results are sensitive to different ways of controlling for fundamentals and highlights the importance of accurate estimation of these housing market fundamentals.

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“…Assumption 1 is standard in the literature (see, for example, Campbell et al ., 2009, Sun & Tsang 2013, Shi, 2017, Gomez‐Gonzalez & Sanin‐Restrepo 2018). The real interest rate

i_{t + 1}

represents the cost of purchasing a house, which could be the cost of borrowing money from banks (mortgage rate) or the opportunity cost (deposit or government bond rate).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Australian Housing Market Booms: Fundamentals or Speculation?*

2020

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This paper investigates the presence of housing bubbles in Australia at the national, capital city and local government area (LGA) levels. We control for housing market demand and supply fundamentals using the technique of Shi (2017), and employ the recursive evolving method proposed by Phillips et al. (2015a,b) for the detection of explosive bubbles. While the national‐level analysis suggests a short‐lived bubble episode (2017Q3) over the entire sample period from 1999 to 2017, the results from the capital‐city‐level data reveal substantial heterogeneity across cities, with only short‐lived and isolated bubbles identified for Sydney and Melbourne during the recent rapid housing price expansions from 2013 to 2017. The LGA‐level analysis suggests that only a small percentage of LGA housing markets are identified to be speculative in Sydney and Melbourne from 2013 to 2017.

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Housing Markets, Regulations and Monetary Policy

Cited by 7 publications

References 23 publications

Understanding Housing Market Volatility

Understanding Housing Market Volatility

Diagnosing housing fever with an econometric thermometer

Australian Housing Market Booms: Fundamentals or Speculation?*

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