Housing career disparities in urban China: A comparison between skilled migrants and locals in Nanjing

Urban Studies ◽  
2018 ◽  
Vol 57 (3) ◽  
pp. 546-562 ◽  
Author(s):  
Can Cui
Keyword(s):  
Social Inequalities ◽  
Housing Prices ◽  
Real Estate Market ◽  
Urban China ◽  
Housing Tenure ◽  
The Real Estate ◽  
Housing Careers ◽  
Housing Career ◽  
Market Fragmentation ◽  
Location Change

The last two decades have witnessed a substantial growth of the owner-occupied housing sector in urban China, where most people tend to follow a conventional life course in terms of ascending the housing ladder towards homeownership. Yet, with skyrocketing housing prices in the real estate market, fragmentation in housing opportunities has become more important in reshaping the structure of social inequalities. This paper investigates the disparities in housing careers between skilled migrants and their local counterparts in Nanjing, focusing on temporal and spatial aspects. Specifically, this paper examines how skilled migrants’ housing tenure and location change over time, to what extent these changes differ from those of skilled locals, and what factors contribute to the disparities between migrants and locals. The results verify that there are indeed disparities in housing careers between migrants and locals, and the foremost difference lies in the tenure, especially the tenure of the first residence. Spatially, migrants exhibit an outward-bound pattern, often associated with the transition from renting to owning. These disparities in housing careers could be primarily attributed not only to the gap of the intergenerational transfer of wealth between migrants and locals, which can be traced back to regional disparities in economic development, but also to the self-selection of migration. While facing skyrocketing housing prices, the timing of making a foray into the housing market is pivotal. This study also revealed the diminishing marginal utility of education that is found in terms of establishing a superior housing career.

scholarly journals COVID-19 Infects Real Estate Markets: Short and Mid-Run Effects on Housing Prices in Campania Region (Italy)

2020 ◽  
Vol 9 (7) ◽  
pp. 114 ◽  
Author(s):  
Vincenzo Del Giudice ◽  
Pierfrancesco De Paola ◽  
Francesco Paolo Del Giudice
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Real Estate Market ◽  
Campania Region ◽  
Real Estate Markets ◽  
The Real ◽  
The Real Estate ◽  
Short Run ◽  
Predator Model ◽  
Real Estate Prices

The COVID-19 (also called “SARS-CoV-2”) pandemic is causing a dramatic reduction in consumption, with a further drop in prices and a decrease in workers’ per capita income. To this will be added an increase in unemployment, which will further depress consumption. The real estate market, as for other productive and commercial sectors, in the short and mid-run, will not tend to move independently from the context of the aforementioned economic variables. The effect of pandemics or health emergencies on housing markets is an unexplored topic in international literature. For this reason, firstly, the few specific studies found are reported and, by analogy, studies on the effects of terrorism attacks and natural disasters on real estate prices are examined too. Subsequently, beginning from the real estate dynamics and economic indicators of the Campania region before the COVID-19 emergency, the current COVID-19 scenario is defined (focusing on unemployment, personal and household income, real estate judicial execution, real estate dynamics). Finally, a real estate pricing model is developed, evaluating the short and mid-run COVID-19 effects on housing prices. To predict possible changes in the mid-run of real estate judicial execution and real estate dynamics, the economic model of Lotka–Volterra (also known as the “prey–predator” model) was applied. Results of the model indicate a housing prices drop of 4.16% in the short-run and 6.49% in the mid-run (late 2020–early 2021).

scholarly journals Modeling the Real Estate Prices in Olsztyn under Instability Conditions

2012 ◽  
Vol 11 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Mirosław Bełej ◽  
Sławomir Kulesza
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Intrinsic Property ◽  
Small Variation ◽  
Smooth Curve ◽  
Real Estate Market ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
The Stability

Abstract The paper deals with the description of the issues related to the dynamics of the real estate market in terms of sharp, unexpected changes in the housing prices which have been observed in the last decade in many European countries due to some macroeconomic circumstances. When such perturbations appear, the real estate market is said to be structurally unstable, since even a small variation in the control parameters might result in a large, structural change in the state of the whole system. The essential problem addressed in the paper is the need to define and discriminate between the intervals of stable and unstable real estate market development with special attention paid to the latter. The research aims at modeling hardly explored field of discontinuous changes in the real estate market in order to reveal the bifurcation edge. Assuming that the periods of sudden price changes reflect an intrinsic property of the real estate market, it is shown that the evolution path draws for most of the time a smooth curve onto the stability area of the equilibrium surface, and only briefly penetrates into the instability area to hop to another equilibrium state.

An Institutional Countermeasure to China's High Urban Housing Prices

2013 ◽  
Vol 448-453 ◽  
pp. 4075-4078
Author(s):  
Jin Zhang
Keyword(s):  
Civil Rights ◽  
Real Estate ◽  
Institutional Economics ◽  
Housing Prices ◽  
Urban Housing ◽  
Real Estate Market ◽  
System Level ◽  
The Real Estate ◽  
Economic Phenomenon ◽  
The Real Estate Market

The economic phenomenon of high urban housing prices in our country reflects asymmetry of rights and interests among government, real estate developers and buyers in essence, and behind this economic phenomenon imbedded financial crisis as well as political and social crisis. Regarding academic thought on the causes of high housing prices in the real estate market such as the theories of supply anddemand, cost, the system, and power imbalance between interest groups, this thesis proceeds institutional analysis, from the perspective of institutional economics, discusses the institutional causes of the persistent existence of four factors theories in the angle of vacancy of civil rights in the supply process of institution and rules, and puts forward policy suggestions of increasing effective supplies of institution in the system level.

scholarly journals Homeownership Rate and Other House Price Determinants Impacts on New House Prices in Colombia

2020 ◽  
pp. 35-50
Author(s):  
Hector Botello-Peñaloza
Keyword(s):  
Real Estate ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Real Estate Market ◽  
The Real Estate ◽  
Homeownership Rate ◽  
Colombian Government ◽  
Price Determinants ◽  
Price Instability

Homeownership remains a preferred form of tenancy in different parts of the world. The attractions of security, stability, investment potential and a sense of pride outweigh the fear of price instability. For this reason, the Colombian government has encouraged in recent years, various demand policies that have sought to promote the increase in the number of homeowners. However, these ideas could have a severe impact on prices in the real estate market. Therefore, this study seeks to examine the effect of homeownership rate on new house prices in an emerging country with low real estate ownership, credit restrictions and average per capita income. The study uses panel data model to examine the influence of housing tenancy and other variables on the variation of housing prices in Colombia. Data were obtained from various sources including the Central Bank of Colombia, Financial Superintendence of Colombia, and National Administrative Department of Statistics of Colombia. The results show that homeownership rates have a positive effect on the price of new homes, which supports the hypothesis of the research. The population growth of the cities is the factor that is most relevant when explaining the price variations.

scholarly journals Impact of Government Measures to Support Mortgage Lending on Housing Affordability in Russia: Regional Evidence

2021 ◽  
Vol 80 (4) ◽  
pp. 98-123
Author(s):  
Ianina Roshchina ◽  
◽  
Natalia Ilyunkina ◽  
Keyword(s):  
Housing Prices ◽  
Real Estate Market ◽  
Mortgage Lending ◽  
Housing Affordability ◽  
Government Support ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Support Housing ◽  
Quantitative Indicators ◽  
The Impact

This study investigates housing affordability in Russia: factors of affordability, quantitative indicators, and government support measures. We are especially interested in the mortgage rate subsidy programmes that were implemented in 2015–2016 and 2020–2021 and their impact on housing affordability indicators. In order to evaluate impact of the first programme, we use a model of the real estate market and we decompose the index of housing affordability into different factors. As a result of our econometric analysis, we conclude that in general the programme was successful. Data about the second programme are not yet sufficient, so we evaluate its impact by a statistical analysis of the dynamics of the main indicators. We conclude that the impact is ambiguous: up until a particular moment (different in different regions), borrowers could benefit from the programme, but after that moment the increase in housing prices caused by the programme itself were exceeding the benefits from the subsidised rates. In conclusion, we provide some methods to improve the effectiveness of government measures to support housing affordability, which could be useful in the development of new programmes.

Research on the Impacts of Real Estate on Economic Growth: A Theoretical Model-Based Analysis

2018 ◽  
Vol 06 (04) ◽  
pp. 1850025
Author(s):  
Xiaoxi ZHANG ◽  
Lu GUO
Keyword(s):  
Economic Growth ◽  
Real Estate ◽  
Low Income ◽  
Housing Prices ◽  
Well Being ◽  
Real Estate Market ◽  
The Real ◽  
The Real Estate ◽  
Pillar Industry ◽  
The Impact

As the pillar industry of China’s economy, the real estate sector has a significant impact on macroeconomic growth. We assume that the first stage of economic actors’ working lives is a low-income one, while their second stage is a high-income one. Then, relying on an Overlapping-Generations Model, we analyze how, via real estate, the behaviors of different income groups affect the macroeconomy. The results show that when the supply of real estate market fluctuates then this has an impact on economic growth, but the extent of the impact depends on the relationship between the real estate and the consumer markets. We also find that when economic actors more greatly prefer their current situations of well-being, no matter whether there takes place or not a new increase in real estate stocks, a negative correlation will exist in the relation between real estate stocks and their prices. Lastly, we come to the conclusion that increases in property taxes can effectively reduce housing prices, but the impact of transaction taxes on housing prices can still not be determined.

scholarly journals Urban Vibrancy: An Emerging Factor that Spatially Influences the Real Estate Market

2020 ◽  
Vol 12 (1) ◽  
pp. 346 ◽  
Author(s):  
Alice Barreca ◽  
Rocco Curto ◽  
Diana Rolando
Keyword(s):  
Real Estate ◽  
Urban Areas ◽  
Housing Prices ◽  
Real Estate Market ◽  
Current Status ◽  
The Real ◽  
The Real Estate ◽  
Activity Intensity ◽  
The Real Estate Market ◽  
Spatial Autoregressive

Urban vibrancy is defined and measured differently in the literature. Originally, it was described as the number of people in and around streets or neighborhoods. Now, it is commonly associated with activity intensity, the diversity of land-use configurations, and the accessibility of a place. The aim of this paper is to study urban vibrancy, its relationship with neighborhood services, and the real estate market. Firstly, it is used a set of neighborhood service variables, and a Principal Component Analysis is performed in order to create a Neighborhood Services Index (NeSI) that is able to identify the most and least vibrant urban areas of a city. Secondly, the influence of urban vibrancy on the listing prices of existing housing is analyzed by performing spatial analyses. To achieve this, the presence of spatial autocorrelation is investigated and spatial clusters are identified. Therefore, spatial autoregressive models are applied to manage spatial effects and to identify the variables that significantly influence the process of housing price determination. The results confirm that housing prices are spatially autocorrelated and highlight that housing prices and NeSI are statistically associated with each other. The identification of the urban areas characterized by different levels of vibrancy and housing prices can effectively support the revision of the urban development plan and its regulatory act, as well as strategic urban policies and actions. Such data analyses support a deep knowledge of the current status quo, which is necessary to drive important changes to develop more efficient, sustainable, and competitive cities.

scholarly journals Bank Competition and Financial Stability: Evidence from the Financial Crisis

2016 ◽  
Vol 51 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Brian Akins ◽  
Lynn Li ◽  
Jeffrey Ng ◽  
Tjomme O. Rusticus
Keyword(s):  
Financial Crisis ◽  
Financial Stability ◽  
Housing Prices ◽  
State Level ◽  
Real Estate Market ◽  
Bank Competition ◽  
Banking Competition ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Recent Financial Crisis

AbstractWe examine the link between bank competition and financial stability using the recent financial crisis as the setting. We utilize variation in banking competition at the state level and find that banks facing less competition are more likely to engage in risky activities, more likely to face regulatory intervention, and more likely to fail. Focusing on the real estate market, we find that states with less competition had higher rates of mortgage approval, experienced greater inflation in housing prices before the crisis, and experienced a steeper decline in housing prices during the crisis. Overall, our study is consistent with greater competition increasing financial stability.

scholarly journals Identifying Real Estate Opportunities Using Machine Learning

2018 ◽  
Vol 8 (11) ◽  
pp. 2321 ◽  
Author(s):  
Alejandro Baldominos ◽  
Iván Blanco ◽  
Antonio Moreno ◽  
Rubén Iturrarte ◽  
Óscar Bernárdez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Housing Prices ◽  
Market Price ◽  
Real Estate Market ◽  
Support Vector ◽  
Regression Problem ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market

The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.

New Mortgage Lenders and the Housing Market*

2020 ◽  
Author(s):  
Nikodem Szumilo
Keyword(s):  
Risk Taking ◽  
Financial Stability ◽  
House Prices ◽  
Housing Prices ◽  
Real Estate Market ◽  
Mortgage Lending ◽  
Mortgage Market ◽  
The Real Estate ◽  
Banking Market ◽  
The Real Estate Market

Abstract This article examines the effect of a new lender’s entry into a local mortgage market on the supply of new loans, housing prices and repossessions in areas around its branches. I use the decision of the European Commission to force the UK’s largest retail bank to divest a part of its business as a shock to the entry of a new lender, and show that incumbent banks increase mortgage lending in areas where the new bank has its branches. Furthermore, house prices increase by around 5% in the real estate market impacted by the shock. Average transaction numbers and mortgage repossession rates also increase in places where the new bank enters. Overall, my results show that increased competition in the banking market can have adverse consequences for risk-taking and financial stability.

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