scholarly journals We zoned for density and got higher house prices: Supply and price effects of upzoning over 20 years

2020 ◽  
Author(s):  
Cameron Murray ◽  
Mark Limb
Keyword(s):  
Regression Models ◽  
House Prices ◽  
Housing Prices ◽  
Housing Markets ◽  
Planning System ◽  
Similar Increase ◽  
Price Effects ◽  
Price Growth ◽  
Study Interval ◽  
Private Housing

Does planning for higher density increase housing development and decrease housing prices? We study the outcomes of planning for density in established suburbs over a twenty-year period using a large site-level dataset on dwelling stock, planning regulations, and prices, in 19 planned densification areas (activity centres) comprising 25,775 sites in Brisbane, Australia. Planning rules in these areas were repeatedly relaxed to allow for higher density; a policy change that should have observable price effects. To study the effect of zoning, we create a variable for each site called zoned capacity, which is the estimated number of additional dwellings able to be built under the planning code. Only 2% of the zoned capacity was taken up in any five-year study interval. Zoned capacity doubled over the whole twenty-year study period (going from 0.9x total dwellings to 1.4x), however despite these changes, 78% of sites with zoned capacity in the first period remained undeveloped. Higher rates of new housing supply are robustly related to higher prices despite demand arguably seeing a similar increase across locations. Our zoned capacity variable has no relationship to price across numerous regression models and is robust to various data selection choices. It could be that planning is not a binding constraint on new housing in Brisbane—yet price growth over our study period is comparable to other Australian cities. This evidence suggests that private housing markets will not rapidly supply new housing and cause significant price reductions, even if the planning system allows it.

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 A dynamic model for housing price spillovers with an evidence from the US and the UK markets

2018 ◽  
Vol 2 (1) ◽  
pp. 70-81 ◽  
Author(s):  
Alper Ozun ◽  
Hasan Murat Ertugrul ◽  
Yener Coskun
Keyword(s):  
New York ◽  
Real Estate ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Housing Markets ◽  
Content Type ◽  
Real Estate Markets ◽  
Country Level ◽  
The Us

Purpose The purpose of this paper is to introduce an empirical model for house price spillovers between real estate markets. The model is presented by using data from the US-UK and London-New York housing markets over a period of 1975Q1-2016Q1 by employing both static and dynamic methodologies. Design/methodology/approach The research analyzes long-run static and dynamic spillover elasticity coefficients by employing three methods, namely, autoregressive distributed lag, the fully modified ordinary least square and dynamic ordinary least squares estimator under a Kalman filter approach. The empirical method also investigates dynamic correlation between the house prices by employing the dynamic control correlation method. Findings The paper shows how a dynamic spillover pricing analysis can be applied between real estate markets. On the empirical side, the results show that country-level causality in housing prices is running from the USA to UK, whereas city-level causality is running from London to New York. The model outcomes suggest that real estate portfolios involving US and UK assets require a dynamic risk management approach. Research limitations/implications One of the findings is that the dynamic conditional correlation between the US and the UK housing prices is broken during the crisis period. The paper does not discuss the reasons for that break, which requires further empirical tests by applying Markov switching regime shifts. The timing of the causality between the house prices is not empirically tested. It can be examined empirically by applying methods such as wavelets. Practical implications The authors observed a unidirectional causality from London to New York house prices, which is opposite to the aggregate country-level causality direction. This supports London’s specific power in the real estate markets. London has a leading role in the global urban economies residential housing markets and the behavior of its housing prices has a statistically significant causality impact on the house prices of New York City. Social implications The house price co-integration observed in this research at both country and city levels should be interpreted as a continuity of real estate and financial integration in practice. Originality/value The paper is the first research which applies a dynamic spillover analysis to examine the causality between housing prices in real estate markets. It also provides a long-term empirical evidence for a dynamic causal relationship for the global housing markets.

scholarly journals Housing Prices, Residential and Nonresidential Investments and Macroeconomic Fluctuations

2017 ◽  
Vol 4 (2) ◽  
pp. 19
Author(s):  
Wei Ma ◽  
Weiqun Li ◽  
Keqin Qu ◽  
John Oden
Keyword(s):  
Financial Crisis ◽  
Business Cycle ◽  
House Prices ◽  
Housing Prices ◽  
Housing Markets ◽  
Macroeconomic Fluctuations ◽  
Empirical Results ◽  
Residential Investment ◽  
The Business Cycle ◽  
Recent Financial Crisis

During the recent financial crisis, the housing markets have played a notably important role in driving macroeconomic fluctuations. We investigate the correlation between housing dynamics and the business cycle for a variety of countries. Our empirical results exhibit the two daunting facts faced by lots of macroeconomic modelers: (i) house prices are highly volatile and closely correlated with the business cycle, which is at odds with the evidence that rental prices are relatively stable and almost uncorrelated with the business cycle; and (ii) residential investment leads the business cycle while nonresidential investment moves contemporaneously with the business cycle.

Housing markets’ linkage between China and Taiwan

2019 ◽  
Vol 12 (5) ◽  
pp. 826-848 ◽  
Author(s):  
Mei-Se Chien ◽  
Neng-Huei Lee ◽  
Chih-Yang Cheng
Keyword(s):  
Economic Integration ◽  
House Prices ◽  
Housing Prices ◽  
Housing Markets ◽  
Wealth Effects ◽  
Time Varying ◽  
Consumer Expenditure ◽  
Content Type ◽  
Cross Border ◽  
Cointegration Tests

Purpose This paper aims to examine the linkage of regional housing markets between Taiwan and China as increasing economic integration. Design/methodology/approach Two time-varying estimations of cointegration tests, Gregory and Hansen (1996) cointegration test with structural break and the recursive coefficients of cointegration (Hansen and Johansen, 1993) are applied to trace the possible dynamic linkage of cross-border regional housing prices between Taiwan and China. Findings First, the estimating results of the long-run relationships show that increasing housing prices in Beijing and Shanghai decrease Taipei’s house prices, while Shenzhen and Chengdu have converse effects. The technologies’ levels of Taiwanese industries surrounding the cities in China will affect the direction of the linkage of regional housing prices between the two economies. Second, in light of causalities of these five housing prices’ changes, Beijing and Shanghai lead Taipei and Shanghai leads Chengdu, which, in turn, leads Shenzhen. Finally, the results of time-varying cointegration tests show that some critical economic and political incidents changed the linkages of housing prices between Taipei and the four cities in China. Originality/value Although some empirical works examined the linkages between cross-border house prices in Europe and the USA, study has looked at the linkages of cross-border housing prices between Taiwan and China. This is an interesting topic insofar as house price integration has implications for wealth effects that feed into consumer expenditure in both Taiwan and China. The empirical evidence overall displays the existence of the integration of regional housing markets between Taiwan and China. For the longer-term future, increasing economic integration between China and other Asia countries will result in greater and more diversified cross-border housing markets and pools of investors.

Do demographic changes affect house prices?

2019 ◽  
Vol 85 (4) ◽  
pp. 305-320
Author(s):  
Kristine Gevorgyan
Keyword(s):  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Demographic Variables ◽  
Generation Model ◽  
Overlapping Generation ◽  
Lower House ◽  
Price Growth ◽  
Demographic Shifts ◽  
Using Data

AbstractThe paper tests the idea that major demographic shifts can affect housing prices. We first build an overlapping generation model and analytically solve for the equilibrium price of the asset. The model predicts that economies with a higher fraction of old people in the overall population have lower house prices. We empirically test this hypothesis using data on house prices and demographic variables from the Organization for Economic Co-operation and Development (OECD). We find that if population growth increases by one percentage point, house price growth increases by 1.4 percentage points.

Examining the spatial and non-spatial linkages between suburban housing markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Morteza Moallemi ◽  
Daniel Melser ◽  
Ashton de Silva ◽  
Xiaoyan Chen
Keyword(s):  
House Prices ◽  
Housing Prices ◽  
Housing Markets ◽  
Geographic Proximity ◽  
Content Type ◽  
Spatial Weights ◽  
Spatial Weights Matrix ◽  
Sar Model ◽  
Spatial Linkages ◽  
The City

Purpose The purpose of this paper is on developing and implementing a model which provides a fuller and more comprehensive reflection of the interaction of house prices at the suburb level. Design/methodology/approach The authors examine how changes in housing prices evolve across space within the suburban context. In doing so, the authors developed a model which allows for suburbs to be connected both because of their geographic proximity but also by non-spatial factors, such as similarities in socioeconomic or demographic characteristics. This approach is applied to modelling home price dynamics in Melbourne, Australia, from 2007 to 2018. Findings The authors found that including both spatial and non-spatial linkages between suburbs provides a better representation of the data. It also provides new insights into the way spatial shocks are transmitted around the city and how suburban housing markets are clustered. Originality/value The authors have generalized the widely used SAR model and advocated building a spatial weights matrix that allows for both geographic and socioeconomic linkages between suburbs within the HOSAR framework. As the authors outlined, such a model can be easily estimated using maximum likelihood. The benefits of such a model are that it yields an improved fit to the data and more accurate spatial spill-over estimates.

scholarly journals DO HIGHER HOUSE PRICES INDICATE HIGHER SAFETY? PRICE VOLATILITY RISK IN MAJOR CITIES IN TAIWAN

2020 ◽  
Vol 24 (3) ◽  
pp. 165-181
Author(s):  
Fang-Ni Chu ◽  
I-Chun Tsai
Keyword(s):  
Housing Market ◽  
House Prices ◽  
Emerging Market ◽  
Housing Prices ◽  
Housing Price ◽  
Housing Markets ◽  
Price Volatility ◽  
Price Inflation ◽  
Volatility Risk ◽  
Price Bubbles

This study investigates the housing market in Taiwan, an emerging market with relatively severe housing price inflation. Using data from the first quarter of 1991 to the second quarter of 2017 for four cities in Taiwan, this study compares the risk transmission and sources of their housing prices. The results reveal that Taipei−Taiwan’s main financial hub−has the highest house prices among the four cities but maintains the lowest risk. Thus, in terms of price volatility risk, Taipei has the safest housing market among the studied cities. Other studies have discussed the potential housing price bubbles in regions with high housing prices but have been unable to explain the continual overheating of the housing markets. The findings of this study reveal that despite having the highest housing prices and the greatest potential bubble, the Taipei housing market has the lowest fluctuation risk, making it the safest market in terms of housing investment. The results of this study imply that Taiwan’s economic development is excessively concentrated in Taipei, causing people to bear low returns and high risk when purchasing real estate in other areas, in turn increasing the continual imbalance between regional housing markets.

The effect of school performance on property values: a literature review and a case study

2017 ◽  
Vol 10 (3) ◽  
pp. 277-302 ◽  
Author(s):  
Larisa Fleishman ◽  
Nir Fogel ◽  
Israela Fridman ◽  
Yaffa Shif
Keyword(s):  
School Performance ◽  
House Prices ◽  
Housing Prices ◽  
Property Values ◽  
Housing Price ◽  
Housing Markets ◽  
Socioeconomic Background ◽  
Content Type ◽  
State Education ◽  
Student Achievements

Purpose This paper, a pioneering one in the Israeli context, aims to augment the research literature on school quality and housing prices by examining the effect of primary-school performance on local property values. It focuses on the main question whether the release of students’ test scores offered households a new source of information with which they could evaluate the quality of schools, thereby affecting local housing markets. Design/methodology/approach Several models that examine a variety of transactions, schools and locality characteristics that affect house prices are estimated. Using different administrative sources of information, a wide array of socioeconomic characteristics of students, parents and homebuyers, as well as locality features, is constructed and merged. This information, combined with students’ scores on Meitzav exams (standardized student achievement tests) in 2009-2012 and house prices, illuminates the relationship between student achievements and the prices of houses purchased within the defined attendance zones. Findings Student achievements, mainly in the state education system, are found to have a positive and statistically significant effect on housing prices. Accurate information published about a certain school that showed much stronger achievements than those yielded by information attainable about the same school before school-level publication, does contribute to boost house prices in the post-publication period. The socioeconomic background of the students’ parents was found to have a significant effect on house prices. The premium for housing value is much higher in the most prestigious, prime demand districts, in which the housing supply is limited and the housing price level is higher than in that the peripheral districts. Originality/value This study not only breaks new ground in the Israeli context but also contributes to the existing literature, by investigating the relation between publishing students’ scores and property values near the same schools, on a national scale. Given that the housing price dynamics and the spatial differentiation of housing stock are extremely hot issues in many European cities, the results of this study could serve as an important tool for better understanding the housing price responses to market incentives, resulting in specific patterns in local housing markets. This paper could be thus applicable in housing policy outline, urban design and planning.

Has tourism driven house prices in Germany? Time-varying evidence since 1870

2021 ◽  
pp. 135481662110088
Author(s):  
Sefa Awaworyi Churchill ◽  
John Inekwe ◽  
Kris Ivanovski
Keyword(s):  
World War Ii ◽  
Historical Data ◽  
House Prices ◽  
Housing Prices ◽  
Time Varying ◽  
Data Set ◽  
Time Series Modelling ◽  
The Impact ◽  
Tourism Flows ◽  
Non Parametric

Using a historical data set and recent advances in non-parametric time series modelling, we investigate the nexus between tourism flows and house prices in Germany over nearly 150 years. We use time-varying non-parametric techniques given that historical data tend to exhibit abrupt changes and other forms of non-linearities. Our findings show evidence of a time-varying effect of tourism flows on house prices, although with mixed effects. The pre-World War II time-varying estimates of tourism show both positive and negative effects on house prices. While changes in tourism flows contribute to increasing housing prices over the post-1950 period, this is short-lived, and the effect declines until the mid-1990s. However, we find a positive and significant relationship after 2000, where the impact of tourism on house prices becomes more pronounced in recent years.

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