Brownfield Areas and Housing Value: Evidence from Milan

Using a transaction price database, in this paper we evaluate the economic effect of abandoned and derelict real estate areas on housing prices in Milan Italy from 1993 to 2016. We find that brownfields are widespread throughout Milan, with larger abandoned and derelict areas prevalent in the suburbs. Standard hedonic price models show that nearby brownfield areas lower housing prices, with stronger effects for larger derelict and abandoned areas. Economic losses are more relevant to houses in the historical city center and are affected by real estate market trends.

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Asymmetric and Spatial Non-Stationary Effects of Particulate Air Pollution on Urban Housing Prices in Chinese Cities

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17207443 ◽

2020 ◽

Vol 17 (20) ◽

pp. 7443

Author(s):

Biao Sun ◽

Shan Yang

Keyword(s):

Real Estate ◽

Quantile Regression ◽

Housing Prices ◽

Spatial Relationship ◽

Well Being ◽

Urban Housing ◽

Real Estate Market ◽

Economic Losses ◽

Full Account ◽

The Impact

Fine particulate matter(PM2.5) pollution will affect people’s well-being and cause economic losses. It is of great value to study the impact of PM2.5 on the real estate market. While previous studies have examined the effects of PM2.5 pollution on urban housing prices, there has been little in-depth research on these effects, which are spatially heterogeneous at different conditional quantiles. To address this issue, this study employs quantile regression (QR) and geographically weighted quantile regression (GWQR) models to obtain a full account of asymmetric and spatial non-stationary effects of PM2.5 pollution on urban housing prices through 286 Chinese prefecture-level cities for 2005–2013. Considerable differences in the data distributions and spatial characteristics of PM2.5 pollution and urban housing prices are found, indicating the presence of asymmetric and spatial non-stationary effects. The quantile regression results show that the negative influences of PM2.5 pollution on urban housing prices are stronger at higher quantiles and become more pronounced with time. Furthermore, the spatial relationship between PM2.5 pollution and urban housing prices is spatial non-stationary at most quantiles for the study period. A negative correlation gradually dominates in most of the study areas. At higher quantiles, PM2.5 pollution is always negatively correlated with urban housing prices in eastern coastal areas and is stable over time. Based on these findings, we call for more targeted approaches to regional real estate development and environmental protection policies.

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EPC Labels and Building Features: Spatial Implications over Housing Prices

Sustainability ◽

10.3390/su13052838 ◽

2021 ◽

Vol 13 (5) ◽

pp. 2838

Author(s):

Alice Barreca ◽

Elena Fregonara ◽

Diana Rolando

Keyword(s):

Real Estate ◽

Spatial Data ◽

Housing Stock ◽

Housing Prices ◽

Energy Performance ◽

Ordinary Least Squares ◽

Real Estate Market ◽

Hedonic Price ◽

Spatial Error ◽

The Impact

The influence of building or dwelling energy performance on the real estate market dynamics and pricing processes is deeply explored, due to the fact that energy efficiency improvement is one of the fundamental reasons for retrofitting the existing housing stock. Nevertheless, the joint effect produced by the building energy performance and the architectural, typological, and physical-technical attributes seems poorly studied. Thus, the aim of this work is to investigate the influence of both energy performance and diverse features on property prices, by performing spatial analyses on a sample of housing properties listed on Turin’s real estate market and on different sub-samples. In particular, Exploratory Spatial Data Analyses (ESDA) statistics, standard hedonic price models (Ordinary Least Squares—OLS) and Spatial Error Models (SEM) are firstly applied on the whole data sample, and then on three different sub-samples: two territorial clusters and a sub-sample representative of the most energy inefficient buildings constructed between 1946 and 1990. Results demonstrate that Energy Performance Certificate (EPC) labels are gaining power in influencing price variations, contrary to the empirical evidence that emerged in some previous studies. Furthermore, the presence of the spatial effects reveals that the impact of energy attributes changes in different sub-markets and thus has to be spatially analysed.

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Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)

Land ◽

10.3390/land10050533 ◽

2021 ◽

Vol 10 (5) ◽

pp. 533

Author(s):

Sheng Li ◽

Yi Jiang ◽

Shuisong Ke ◽

Ke Nie ◽

Chao Wu

Keyword(s):

Real Estate ◽

Housing Prices ◽

Real Estate Market ◽

Influential Factors ◽

City Centre ◽

Gradient Boosting ◽

Hedonic Price Model ◽

Hedonic Price ◽

Price Model ◽

Extreme Gradient Boosting

The characteristics of housing and location conditions are the main drivers of spatial differences in housing prices, which is a topic attracting high interest in both real estate and geography research. One of the most popular models, the hedonic price model (HPM), has limitations in identifying nonlinear relationships and distinguishing the importance of influential factors. Therefore, extreme gradient boosting (XGBoost), a popular machine learning technology, and the HPM were combined to analyse the comprehensive effects of influential factors on housing prices. XGBoost was employed to identify the importance order of factors and HPM was adopted to reveal the value of the original non-market priced influential factors. The results showed that combining the two models can lead to good performance and increase understanding of the spatial variations in housing prices. Our work found that (1) the five most important variables for Shenzhen housing prices were distance to city centre, green view index, population density, property management fee and economic level; (2) space quality at the human scale had important effects on housing prices; and (3) some traditional factors, especially variables related to education, should be modified according to the development of the real estate market. The results showed that the demonstrated multisource geo-tagged data fusion framework, which integrated XGBoost and HPM, is practical and supports a comprehensive understanding of the relationships between housing prices and influential factors. The findings in this article provide essential implications for informing equitable housing policies and designing liveable neighbourhoods.

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What drives housing prices, rent and new construction in China

International Journal of Housing Markets and Analysis ◽

10.1108/ijhma-12-2016-0080 ◽

2017 ◽

Vol 10 (5) ◽

pp. 662-686

Author(s):

Dimitrios Staikos ◽

Wenjun Xue

Keyword(s):

Real Estate ◽

Empirical Test ◽

Housing Prices ◽

Panel Cointegration ◽

Real Estate Market ◽

Estimation Methods ◽

Content Type ◽

New Construction ◽

Using Data ◽

First Time

Purpose With this paper, the authors aim to investigate the drivers behind three of the most important aspects of the Chinese real estate market, housing prices, housing rent and new construction. At the same time, the authors perform a comprehensive empirical test of the popular 4-quadrant model by Wheaton and DiPasquale. Design/methodology/approach In this paper, the authors utilize panel cointegration estimation methods and data from 35 Chinese metropolitan areas. Findings The results indicate that the 4-quadrant model is well suited to explain the determinants of housing prices. However, the same is not true regarding housing rent and new construction suggesting a more complex theoretical framework may be required for a well-rounded explanation of real estate markets. Originality/value It is the first time that panel data are used to estimate rent and new construction for China. Also, it is the first time a comprehensive test of the Wheaton and DiPasquale 4-quadrant model is performed using data from China.

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Teoretyczne i metodyczne aspekty wyznaczania indeksów cen na rynku mieszkaniowym

10.18559/978-83-66199-08-8 ◽

2018 ◽

Author(s):

Radosław Trojanek

Keyword(s):

Real Estate ◽

Housing Prices ◽

Real Estate Market ◽

Secondary Market ◽

Property Right ◽

Sources Of Information ◽

Single Family ◽

Starting Point ◽

Price Indexes

In the book, an attempt was made to catalogue knowledge concerning the importance of research into the dynamics of housing prices for social and economic development. The analysis of the experience of countries with well-developed real estate markets in the aspect of building price indexes was carried out. Based on original databases of asking and transaction prices, price indexes were built, which were then subjected to numerous resistance tests. The aims of these research tasks were as follows: 1) to examine the quality of offers for sale as a source of information about changes in the real estate market, 2) to find out whether the repeat sales method can be used for building price indexes and to critically assess this method in terms of the stability of the obtained results, 3) to analyze hedonic methods and indicate the preferred one in terms of the ratio of the quality of results to how time-consuming and cost-intensive it is to build such indexes, 4) to establish the importance of methods and sources of information for building price indexes in different time horizons, 5) to identify how important it is for the fluctuation of price indexes if the cooperative property right to a flat is not taken into account. In order to perform the research tasks and accomplish the goals scopes of the work were defined. The subject followed the aim of the study and refers to prices in the secondary housing market, encompassing both the property right and cooperative property right to a flat or house. The broad scope concerns the discussion in the general part, being narrowed down to the secondary market of flats located in multi-family and single-family buildings. The time scope covers the years 2000-2015, which is connected to the range of empirical studies carried out. They focused both on actual transactions and on offers of flats for sale. On this basis, we built databases which served as the starting point for further analyses. The study involved transactions and offers in the area of Poznan.

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Did the COVID-19 Pandemic Crisis Affect Housing Prices Evenly in the U.S.?

Sustainability ◽

10.3390/su132112277 ◽

2021 ◽

Vol 13 (21) ◽

pp. 12277

Author(s):

Xinba Li ◽

Chuanrong Zhang

Keyword(s):

Real Estate ◽

Spatial Patterns ◽

Price Change ◽

Housing Prices ◽

Housing Price ◽

Real Estate Market ◽

Moran’S I ◽

Moran's I ◽

Price Changes ◽

The U.S

While it is well-known that housing prices generally increased in the United States (U.S.) during the COVID-19 pandemic crisis, to the best of our knowledge, there has been no research conducted to understand the spatial patterns and heterogeneity of housing price changes in the U.S. real estate market during the crisis. There has been less attention on the consequences of this pandemic, in terms of the spatial distribution of housing price changes in the U.S. The objective of this study was to explore the spatial patterns and heterogeneous distribution of housing price change rates across different areas of the U.S. real estate market during the COVID-19 pandemic. We calculated the global Moran’s I, Anselin’s local Moran’s I, and Getis-Ord’s statistics of the housing price change rates in 2856 U.S. counties. The following two major findings were obtained: (1) The influence of the COVID-19 pandemic crisis on housing price change varied across space in the U.S. The patterns not only differed from metropolitan areas to rural areas, but also varied from one metropolitan area to another. (2) It seems that COVID-19 made Americans more cautious about buying property in densely populated urban downtowns that had higher levels of virus infection; therefore, it was found that during the COVID-19 pandemic year of 2020–2021, the housing price hot spots were typically located in more affordable suburbs, smaller cities, and areas away from high-cost, high-density urban downtowns. This study may be helpful for understanding the relationship between the COVID-19 pandemic and the real estate market, as well as human behaviors in response to the pandemic.

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The Impacts of Traffic Noise on House Renting and Selling Prices Concerning Residential Areas Case of Sulaimaniyah City Center

10.24086/afs2020/paper.214 ◽

2021 ◽

Author(s):

Ayoub Smaqaey ◽

◽

Mohammed AbdulKareem ◽

Meryem Komşu ◽

◽

...

Keyword(s):

Real Estate ◽

High Efficiency ◽

Negative Impact ◽

Traffic Noise ◽

Noise Pollution ◽

Real Estate Market ◽

Residential Areas ◽

City Center ◽

The Government ◽

The Impact

The purposes of this research are to examine the impact of traffic noise on the sale and rent prices of the housing real estate in the Sulaimaniyah city center. Besides, highlight the concept of traffic noise pollution in general and in particular in the Sulaimaniyah city center. Thus, people have the right to choose the nature of the acoustic environment, as others should not impose it, the problem of traffic noise considered as one of the main problems that have imposed on the people in Sulaimaniyah city center. Which began to take severe economic and social dimensions, affects the decision-making process in the real estate market. Moreover, consequently, this research analyzes the impact of traffic noise pollution in the sale and rent prices of residential property in Sulaimaniyah city center, the results of the research have confirmed a clear and negative impact the traffic noise on residential real estate prices in Sulaimaniyah city center. Finally, the research indorsed range of important recommendations, such as necessity control the noise pollution at the level of governments and companies, either at the companies’ level by choosing vehicles that release less sound and the use of sound control devices of high efficiency. Either at the government level to determine the volume level or prevent annoying noises (painful), through legislation and laws of environmental protection and impose fees and raise awareness.

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

Social Sciences ◽

10.3390/socsci9070114 ◽

2020 ◽

Vol 9 (7) ◽

pp. 114 ◽

Cited By ~ 3

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).

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Modeling the Real Estate Prices in Olsztyn under Instability Conditions

Folia Oeconomica Stetinensia ◽

10.2478/v10031-012-0008-7 ◽

2012 ◽

Vol 11 (1) ◽

pp. 61-72 ◽

Cited By ~ 5

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.

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Housing Market Hedonic Price Study Based on Boosting Regression Tree

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2017.p1040 ◽

2017 ◽

Vol 21 (6) ◽

pp. 1040-1047 ◽

Cited By ~ 3

Author(s):

Guangtong Gu ◽

Bing Xu ◽

◽

Keyword(s):

Real Estate ◽

Housing Prices ◽

Regression Tree ◽

Gradient Boosting ◽

Hedonic Price ◽

Tree Model ◽

Policy Makers ◽

Real Estate Markets ◽

Architectural Features ◽

Real Estate Prices

Based on the purchase price data of new real estate markets three cities in China, Beijing, Shanghai, and Guangzhou, including architectural features, neighborhood property features, and location features, in this study a boosting regression tree model was built to study the factors and the influence path of housing prices from the microcosmic perspective. First, a classical hedonic price model was constructed to analyze and compare the significant effect factors on housing prices in the market segments of the three cities. Second, the gradient boosting regression tree method that is proposed in this paper was applied to the three markets in combination to analyze the influence paths and factors and the importance of the type of housing hedonic price. The influence paths of housing hedonic prices and decision tree rules are visualized. The significant housing features are effectively extracted. Finally, we present three main conclusions and several suggestions for policy makers to improve urban functions while stabilizing real estate prices.

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