scholarly journals Speculative housing markets and rent control: insights from nonlinear economic dynamics

2021 ◽  
Author(s):  
Noemi Schmitt ◽  
Frank Westerhoff
Keyword(s):  
Housing Market ◽  
Housing Stock ◽  
House Price ◽  
Housing Markets ◽  
Market Model ◽  
Market Dynamics ◽  
Rent Control ◽  
Forecasting Accuracy ◽  
Expectation Formation ◽  
Rent Level

AbstractWe propose a novel housing market model to explore the effectiveness of rent control. Our model reveals that the expectation formation and learning behavior of boundedly rational homebuyers, switching between extrapolative and regressive expectation rules subject to their past forecasting accuracy, may create endogenous housing market dynamics. We show that policymakers may use rent control to reduce the rent level, although such policies may have undesirable effects on the house price and the housing stock. However, we are also able to prove that well-designed rent control may help policymakers to stabilize housing market dynamics, even without creating housing market distortions.

scholarly journals HOUSING MARKETS, EXPECTATION FORMATION AND INTEREST RATES

2020 ◽  
pp. 1-42
Author(s):  
Carolin Martin ◽  
Noemi Schmitt ◽  
Frank Westerhoff
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Housing Market ◽  
House Prices ◽  
Housing Markets ◽  
Central Banks ◽  
Market Model ◽  
Expectation Formation ◽  
The Interest Rate ◽  
Stock Flow

Based on a behavioral stock-flow housing market model in which the expectation formation behavior of boundedly rational and heterogeneous investors may generate endogenous boom-bust cycles, we explore whether central banks can stabilize housing markets via the interest rate. Using a mix of analytical and numerical tools, we find that the ability of central banks to tame housing markets by increasing the base (target) interest rate, thereby softening the demand pressure on house prices, is rather limited. However, central banks can greatly improve the stability of housing markets by dynamically adjusting the interest rate with respect to mispricing in the housing market.

Housing market networks in China's major cities: a conditional causality approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lu Yang ◽  
Nannan Yuan ◽  
Shichao Hu
Keyword(s):  
Housing Market ◽  
Granger Causality ◽  
House Price ◽  
Housing Markets ◽  
Content Type ◽  
Scale Free ◽  
Second Tier ◽  
The Difference ◽  
Secondary Housing Market ◽  
Market Networks

PurposeTo explore the state of this conditional Granger causality when other cities are not factors, we investigate housing market networks in China's major cities by using a combination of conditional Granger causality and network analysis.Design/methodology/approachAlthough housing market networks have been well discussed for different countries, the question of housing market networks in China's major cities based on the conditional causality perspective has yet to be answered.FindingsWe discover that second-tier cities are more influential than first-tier cities. Although the connectivity of the primary housing market is more complex than the diversified connectivity observed in the secondary housing market, both markets are scale-free networks that exhibit high stability. Moreover, we reveal that geographic conditions and economic development jointly determine the housing market's modular hierarchical structure. Our results provide meaningful information for both Chinese policymakers and investors.Originality/valueBy excluding the influence of other cities, our conditional Granger causality identifies the true casual relation between cities' housing markets. Moreover, it is the first paper to consider the primary housing market and secondary housing market separately. Specifically, Chinese prefer new house rather than second-hand house from both speculative and self-housing. Generally speaking, the new house price is lower than the second-hand house price since the new house is off-plan property. Therefore, understanding the difference between primary and secondary housing markets will provide useful information for both policymakers and speculators.

A look into German housing markets: A bubble call?

2018 ◽  
Vol 21 (4) ◽  
pp. 289-301
Author(s):  
Jan R. Kim ◽  
Gieyoung Lim
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Housing Markets ◽  
Present Value ◽  
Present Value Model ◽  
Price Bubbles ◽  
Steep Rise ◽  
Value Model ◽  
German Housing Market

The steep rise in German house prices in recent years raises the question of whether a speculative bubble has already emerged. Using a modified present-value model, we estimate the size of speculative house price bubbles in the German housing market. We do not find evidence for positive bubble accumulation in recent years, and interpret the current bullish run as reflecting the correction of house prices that have been undervalued for more than 10 years. With house prices close to their fair values as of 2018:Q1, our answer to the question is, ‘Not yet, but it is likely soon’.

scholarly journals Club convergence of house prices: Evidence from China’s ten key cities

2015 ◽  
Vol 29 (24) ◽  
pp. 1550181 ◽  
Author(s):  
Hao Meng ◽  
Wen-Jie Xie ◽  
Wei-Xing Zhou
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Economic Recession ◽  
Housing Markets ◽  
Extensive Study ◽  
Ripple Effect ◽  
Chinese Stock Market ◽  
Price Growth ◽  
Chinese Housing Market

The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a marked fall during the global financial tsunami and China’s economy has also slowed down by about 2%–3% when measured in GDP. Nevertheless, the housing markets in diverse Chinese cities seemed to continue the almost nonstop mania for more than 10 years. However, the structure and dynamics of the Chinese housing market are less studied. Here, we perform an extensive study of the Chinese housing market by analyzing 10 representative key cities based on both linear and nonlinear econophysical and econometric methods. We identify a common collective driving force which accounts for 96.5% of the house price growth, indicating very high systemic risk in the Chinese housing market. The 10 key cities can be categorized into clubs and the house prices of the cities in the same club exhibit an evident convergence. These findings from different methods are basically consistent with each other. The identified city clubs are also consistent with the conventional classification of city tiers. The house prices of the first-tier cities grow the fastest and those of the third- and fourth-tier cities rise the slowest, which illustrates the possible presence of a ripple effect in the diffusion of house prices among different cities.

scholarly journals The uneven distribution of capital gains in times of socio-spatial inequality: Evidence from Swedish housing pathways between 1995 and 2010

Urban Studies ◽  
2017 ◽  
Vol 55 (12) ◽  
pp. 2721-2742 ◽  
Author(s):  
Barend Wind ◽  
Lina Hedman
Keyword(s):  
Housing Market ◽  
House Price ◽  
Spatial Inequality ◽  
Capital Gains ◽  
Uneven Distribution ◽  
Market Dynamics ◽  
Housing Wealth ◽  
Investment Capital ◽  
Gains And Losses ◽  
Housing Pathways

Housing wealth is the largest component of wealth for a majority of Swedish households. Whereas investments in housing are merely defined by income, the returns on this investment (capital gains) are dependent on local housing market dynamics. Since the 1990s, local housing market dynamics in Swedish cities have been altered by the upswing in levels of socio-spatial inequality. The simultaneous up- and downgrading of neighbourhoods is reflected in house price developments and exacerbates the magnitude of capital gains and losses. This article proposes that the selective redirection of housing pathways that causes an upswing in socio-spatial inequality translates into an uneven distribution of capital gains as well. A sequence analysis of the housing pathways of one Swedish birth cohort (1970–1975), based on population-wide register data (GeoSweden), is used to explain differences in capital gains between different social groups in the period 1995–2010. The results indicate higher capital gains for individuals with higher incomes and lower gains for migrants. When socio-spatial inequality increases, the more resourceful groups can use their economic and cultural capital to navigate through the housing market in a more profitable way.

Heterogeneous housing markets: structural implications for pricing and risk

2014 ◽  
Vol 7 (3) ◽  
pp. 383-396 ◽  
Author(s):  
Trond A. Borgersen
Keyword(s):  
Housing Market ◽  
House Prices ◽  
House Price ◽  
Housing Markets ◽  
Value Added ◽  
Price Risk ◽  
Content Type ◽  
Price Response ◽  
Market Framework ◽  
House Price Risk

Purpose – The purpose of this paper is to compare the structure of risk and the structure of pricing in housing markets where the interaction between segments is taken into account with the structures that come about in a housing market approach that ignores this interplay. Knowing how most empirical assessments of whether housing markets are in or out of equilibrium is related to macroeconomic variables and is ignoring the interplay between segments our aim is to highlight the extent to which a homogeneous market framework underestimates pricing and risk in real housing markets. Design/methodology/approach – Framed in terms of a linearized housing market with two segments, the author derives expressions for house prices and house price risk in three scenarios. The author compares the structure of pricing and the structure of risk in a homogeneous housing market with those of two distinct heterogeneous housing markets where segments are linked as well analyzing as how prices and risk responds to shocks. Findings – The author derives expressions for market segment prices and for the house price index in three distinct housing market scenarios and shows how heterogeneous housing market frameworks produce both expressions for house prices and for house price risk, as well as a response in both risk and prices to shocks to demand, that deviate from those of a homogeneous housing market framework. While significantly underestimating house price risk a homogeneous framework might also be taken by surprise of the price response accompanying shocks to demand. Originality/value – The authors' simplistic expressions for house prices and house price risk provides a framework for bringing two distinct theoretical housing market camps onto the same playing field. The approach shows the value added of taking the interplay between market segments into account when analyzing housing market developments.

Equity induced up-trading and the housing market structure

2014 ◽  
Vol 7 (2) ◽  
pp. 204-217 ◽  
Author(s):  
Trond-Arne Borgersen
Keyword(s):  
Market Structure ◽  
Housing Market ◽  
House Price ◽  
Market Model ◽  
Future Research ◽  
Home Equity ◽  
Market Segments ◽  
Content Type ◽  
Price Effects ◽  
Short Run

Purpose – The purpose of this paper is to highlight the importance of home equity and the interplay between market segments for housing market developments. The intention is to show that it is not only the aggregate equity gain but also the distribution of equity gains between segments that matter for how shocks to income impact house prices. Design/methodology/approach – The paper sets out a linear housing market model with three segments. Households trade up a housing ladder and link the three segments for owner-occupied housing. The up-trading is equity-induced. An expression for the house price index, which is related to the market segment prices both directly through the segment size and indirectly through a segment position on the housing ladder is derived. The author considers the price effects of shocks to income in four housing market regimes. Findings – The heterogeneous housing market model shows how the interplay between segments impacts housing markets. When considering shocks to income, short-run deviations in the price-to-income (PTI) ratio compared to their long-run equilibrium due to equity-induced up-trading were found. The extent of PTI overshooting is related to the intensity of equity-induced up-trading between different segments. The market structure necessary to eliminate such overshooting is contingent on the distribution of equity gains between segments. Finally, the paper shows how the price effects of macroprudential interventions might be non-negligible when indirect effects are taken into account. Originality/value – The linear housing market model with three market segments introduces a framework where the intensity of equity-induced up-trading in different market segments can be analyzed. This distributional aspect is, to the best of the author's knowledge, novel. The context-specific relation between housing market structure, equity-induced up-trading and short-run deviations in the PTI ratio provides a foundation for future research.

A Local Housing Market Model with Spatial Interaction and Land-Use Planning Controls

2005 ◽  
Vol 37 (9) ◽  
pp. 1637-1649 ◽  
Author(s):  
Chris Leishman ◽  
Glen Bramley
Keyword(s):  
Pilot Study ◽  
Housing Market ◽  
Land Use Planning ◽  
Spatial Interaction ◽  
Local Level ◽  
House Price ◽  
Market Model ◽  
Land Supply ◽  
Price Levels ◽  
House Building

There have been relatively few attempts to construct local housing market models in the United Kingdom—particularly models with an explicit treatment of land supply. In this paper we report the results of a pilot study designed to test the practicability of estimating a system of equations which describe housing market dynamics at the local level. Former district council areas in Central Scotland are used as a proxy for local housing markets within a region, thereby providing a panel dataset. A simple supply — demand system with separate equations for inward and outward household migration is modelled using two-stage least squares. The empirical results are varied, with some equations and coefficients performing more closely in line with prior expectations than others. House price levels are explained largely with reference to household income, socioeconomic status, and past levels of house price growth. Higher price levels and higher deprivation diminish inward migration. There are also suggestions in the results that higher rates of new-build supply partly cause higher inward migration. The rate of outward migration increases with ethnicity and wealth and decreases with deprivation. The empirical performance of the new-build supply equation is poor although the results do yield some interesting insights. House building output generally decreases as the proportion of ‘small’ sites in the land supply increases. There is also evidence that house building output decreases as land supply in neighbouring areas increases. We conclude the paper by outlining further directions for modelling prices, supply, and migration at local housing market level. In particular, the case is made for further work involving the collection of wider and longer panel datasets and for extending the pilot study work beyond Scotland.

scholarly journals Seeking Alpha in the Housing Market

2021 ◽  
Author(s):  
Michael LaCour-Little ◽  
Jing Yang
Keyword(s):  
Housing Market ◽  
Price Index ◽  
Reality Television ◽  
House Price ◽  
Housing Markets ◽  
Short Term ◽  
Television Shows ◽  
Acquisition Strategies ◽  
Time Periods ◽  
Nationally Representative

AbstractWe examine short term trades in the housing market over the period 2000–2013 using nationally representative data across multiple U.S. housing markets. Such trades, often characterized as “house flipping”, have gained currency in recent years with reality television shows depicting success and failure. We find evidence of returns in excess of market house price index growth (which we call alpha) during certain time periods with results that also vary across distressed versus non-distressed acquisition strategies.

Housing market heterogeneity and cluster formation: evidence from Poland

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mateusz Tomal
Keyword(s):  
Housing Market ◽  
Price Level ◽  
Driving Forces ◽  
Housing Stock ◽  
Cluster Formation ◽  
Housing Markets ◽  
Content Type ◽  
Business Entities ◽  
Socio Economic Factors

Purpose This study aims to identify clusters amongst the county housing markets in Poland, taking into account the criteria of size and quality of the housing stock, as well as price level. In addition, this work is intended to detect the socio-economic factors driving the cluster formation. Design/methodology/approach To group the studied housing markets into homogeneous clusters, this analysis uses a proprietary algorithm based on taxonomic and k-means++ methods. In turn, the generalised ordered logit (gologit) model was used to explore factors influencing the cluster formation. Findings The results obtained revealed that Polish county housing markets can be classified into three or four homogeneous clusters in terms of the size and quality of the housing stock and price level. Furthermore, the results of the estimation of the gologit models indicated that population density, number of business entities and the level of crime mainly determine the membership of a given housing market in a given cluster. Originality/value In contrast to previous studies, this is the first to examine the existence of homogeneous clusters amongst the county housing markets in Poland, taking into account the criteria of size and quality of the housing stock, as well as price level simultaneously. Moreover, this work is the first to identify the driving forces behind the formation of clusters amongst the surveyed housing markets.

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