scholarly journals Temporal and Spatial Effects of Urban Center on Housing Price — A Case Study on Hangzhou, China

2018 ◽  
Vol 5 (1) ◽  
pp. 89 ◽  
Author(s):  
Luhong Chu ◽  
Haizhen Wen
Keyword(s):  
Rapid Development ◽  
Housing Prices ◽  
Housing Price ◽  
Urban Land ◽  
Urban Housing ◽  
Spatial Effects ◽  
Hedonic Price ◽  
Urban Center ◽  
Center City ◽  
The Impact

<em>With the acceleration of urbanization and the rapid development of real estate, people pay more and more attention to the change of urban housing prices. Over time, the change of city center will inevitably affect the urban land or housing prices, which is reflected in the spatial distribution of urban land or housing prices. Therefore, this article attempts to explore the impact of urban center on housing prices from the perspective of multi-center city and study separately from two aspects of time and space. This paper takes the six main urban districts of Hangzhou as the research scope. At the time level, we select the residential data from 2007 to 2015 to construct models respectively based on the hedonic price theory and find that the influence of different urban center on housing price shows a certain change with time. On the spatial level, this paper choses the residential data in 2012 to construct geographic weighted regression model and the result shows that the impact of three centers on housing prices shows a certain degree of spatial heterogeneity.</em>

scholarly journals Heterogeneity Analysis of Urban Rail Transit on Housing with Different Price Levels: A Case Study of Chengdu, China

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1330
Author(s):  
Pengyu Ren ◽  
Zhaoji Li ◽  
Weiguang Cai ◽  
Lina Ran ◽  
Lei Gan
Keyword(s):  
Housing Prices ◽  
Housing Price ◽  
Ordinary Least Squares ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Hedonic Price ◽  
Least Squares Regression ◽  
Price Levels ◽  
Urban Rail ◽  
The Impact

The impact of urban rail transit on housing prices has attracted the extensive attention of scholars, but few studies have explored the heterogeneous impact of rail transit on housing prices with different price levels. To solve this problem, we adopted the hedonic price model based on ordinary least squares regression as a supplementary method of quantile regression to study the heterogeneous impact of the Chengdu Metro system on low-, middle-, and high-priced housing. The result shows that the housing price rises first, then falls with the distance from the housing to the nearest subway station. Besides, the influence of transportation accessibility on low-, middle-, and high-priced housing decreases progressively. This research can provide a reference for the government’s transportation planning and decision-making.

scholarly journals The Impact of High-Speed Rail on Housing Prices: Evidence from China’s Prefecture-Level Cities

2019 ◽  
Vol 11 (13) ◽  
pp. 3681
Author(s):  
Rong Wang ◽  
Li Ye ◽  
Liwen Chen
Keyword(s):  
High Speed ◽  
Rapid Development ◽  
Housing Prices ◽  
Housing Price ◽  
Significance Test ◽  
Chinese Government ◽  
High Speed Rail ◽  
Local Conditions ◽  
Rail Line ◽  
The Impact

The rapid development of the high-speed rail (HSR) network enhanced the regional accessibility between cities, drove the rise in cities’ investment levels, and expanded the activity radius of the labor force, causing changes in housing prices along the rail lines. Based on panel data of 285 cities in China from 2008–2016, this study used the difference-in-difference based on propensity score matching (PSM-DID) method to calculate the impact of HSR on housing prices. The conclusions of the study indicated that, at the regional level, HSR significantly promoted the rise in housing prices in HSR cities along the rail line. HSR had a positive effect on housing prices, where the coefficient of HSR influence was 0.1511 and passed a 1% significance test. From the perspective of the combination of sub-regional and sub-city scales, HSR mainly played a significant role in promoting housing prices in “small and medium-sized cities” and “central and western cities”, especially in small and medium-sized cities in the central and western regions; in general, HSR can narrow the housing price gap between “small and medium-sized HSR cities” in the central and western regions and large HSR cities in the east region. Lastly, the results of the intermediary mechanism test showed that the income level of residents and employment levels played an intermediary role in the influence of HSR on the housing prices of cities along the rail line. Thus, this paper suggests that the Chinese government needs to formulate housing price control policies that suit local conditions according to the characteristics of different cities.

scholarly journals THE EFFECTS OF A SHOPPING MALL ON HOUSING PRICES: A CASE STUDY IN HANGZHOU

2018 ◽  
Vol 23 (1) ◽  
pp. 65-80 ◽  
Author(s):  
Ling Zhang ◽  
Jiantao Zhou ◽  
Eddie C. M. Hui ◽  
Haizhen Wen
Keyword(s):  
Housing Prices ◽  
Housing Price ◽  
Shopping Mall ◽  
Hedonic Price ◽  
Shopping Malls ◽  
Time Dimension ◽  
Positive Effects ◽  
The Impact ◽  
Positive Effect

There are few studies on the externalities of shopping malls affecting the housing market. This study aims to discuss two issues: (1) What is the intensity of the impact of a shopping mall? (2) When does the external influence of a shopping mall begin to reveal itself? The West Intime Shopping Mall in Hangzhou offers a unique situation to research the questions. By dividing the study area into nine blocks, using hedonic price theory, and the price gradient approach with housing price data from 2011 to 2015, we found that in the space dimension, the mall exerted a significantly positive effect on the housing prices of nearby blocks. With the increase in distance from the mall, the positive effect decreased. There were more significantly positive effects in blocks far away from the city center. In the time dimension, the effects of West Intime did not reveal themselves until the mall had started to operate and gradually matured over time, implying that the mall did not have the obvious expected impact on housing prices before the mall had begun operating.

scholarly journals The Impact of Micro-Level Influencing Factors on Home Value: A Housing Price-Rent Comparison

2018 ◽  
Vol 10 (12) ◽  
pp. 4343 ◽  
Author(s):  
Nana Cui ◽  
Hengyu Gu ◽  
Tiyan Shen ◽  
Changchun Feng
Keyword(s):  
Influencing Factors ◽  
School Quality ◽  
School Attendance ◽  
Housing Prices ◽  
Housing Price ◽  
Living Environment ◽  
Hedonic Price ◽  
Micro Level ◽  
Employment Center ◽  
The Impact

The housing sales market in China has flourished and gained considerable interest, while the housing rental market has lagged behind and been ignored over the past two decades. With the acceleration of urbanization, the housing rental demand is rising rapidly. Exploring and comparing the influencing factors on housing sale prices and rental prices has significance for sustainable urban planning and management. Using house purchase transaction and rent transaction data in 2017, as well as the average housing price and rent data in 2016 in Beijing, China, this paper compares the spatial distribution and it employs the hedonic price model and quantile regression model to quantify the average and distributional effects of micro-level influencing factors on housing prices and housing rents. Results show that housing prices and housing rents both have a decentralized distribution with multiple centers, but rents of residential communities with high housing prices may not necessarily be high. Both homeowners and renters prefer properties with good structural, locational, and neighborhood characteristics, as well as a good school attendance zone, whereas they still differ in terms of preferences. Homeowners prefer a higher-quality living environment. Renters are more concerned with proximity to an employment center and public transit convenience. Moreover, the price premium of school quality for homeowners exceeds the premium for renters. Higher-priced homeowners or renters differ in the preferences from lower-priced homeowners or renters. Higher-priced homeowners and higher-priced renters are more willing to live in property with a larger number of bedrooms, proximity to a major employment center, park, or school, as well as a location in a school attendance zone with higher school quality.

scholarly journals Mixed Land Use Evaluation and Its Impact on Housing Prices in Beijing Based on Multi-Source Big Data

Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1103
Author(s):  
Hanbing Yang ◽  
Meichen Fu ◽  
Li Wang ◽  
Feng Tang
Keyword(s):  
Land Use ◽  
Big Data ◽  
Housing Prices ◽  
Housing Price ◽  
Urban Center ◽  
Land Use Types ◽  
Mixed Land Use ◽  
Urban Problems ◽  
The Impact ◽  
Life Circle

The tense relationship between the supply and demand of land resources and the past spatial expansion of urban development in Beijing have brought many urban problems. Mixed land use is considered to be able to solve these urban problems as well as promote sustainable urban development. In this context, this study uses multi-source big data such as POI, OpenStreetMap and web crawler data to construct current land-use data of the area within the sixth ring road of Beijing, and then uses the entropy index and type number index to analyze the spatial distribution and aggregation characteristics of the mixed land-use level. Finally, a multi-scale geographically weighted regression is applied to explore the impact of the block and life circle scale mixed land use on housing prices. The results show that: (1) the accuracy of land use data obtained by using multi-source big data is high, and the consistency with the real land use situation is as high as 82.67%. (2) the mixed land use level in the study area is higher in the urban center and lower in the periphery of the city. However, it does not show the spatial distribution characteristics gradually decreasing with the increase of the distance from the urban center but shows that the area from the third to the fifth ring road is the highest. (3) the impact of block scale and life circle scale mixed land use on housing price is different. The type number index has a negative effect on the housing price in block scale mixed land use, while the entropy index has a positive effect on the housing price in life circle scale mixed land use. Based on the existing “bottom-up” individual-dominant development mode, the government of Beijing should issue relevant policies and documents to give “top-down” control and guidance in the future, so as to promote the maximization of the benefits of mixed land use. Furthermore, in the practice of mixed land use in Beijing, land use types should be reduced at the block scale and the area of different land use types should be balanced at the life circle scale.

scholarly journals A Study on the Impact of Unconventional (and Conventional) Drilling on Housing Prices in Central Oklahoma

2021 ◽  
Vol 13 (24) ◽  
pp. 13880
Author(s):  
Kangil Lee ◽  
Brian Whitacre
Keyword(s):  
Housing Prices ◽  
Housing Price ◽  
Local Environment ◽  
Underground Water ◽  
Environmental Pollutant ◽  
Hedonic Price ◽  
Development Activity ◽  
Home Buyer ◽  
The Impact ◽  
Unconventional Drilling

Shale energy development activity may benefit some aspects of a regional economy (such as increased jobs or tax revenue); however, there may also be negative impacts to the local environment, such as noise and underground water contamination. We study the impact of unconventional drilling activity on housing price in an area of the country with a long history of crude oil production. A prospective home buyer may want to avoid a place near sites that have been drilled using unconventional drill technologies such as horizontal fracturing. Adopting a hedonic price model, we estimate the impact of distance to and density of unconventional drilling on housing prices in two central counties in Oklahoma during the period 2001–2016. We also apply a semiparametric approach to deal with the possibility that the relationship between an environmental pollutant source and housing price is nonlinear. The empirical results are consistent in terms of physical housing characteristics and locational aspects in all cases, with drilling activity having only a minimal effect in benchmark models. Further, the semiparametric estimation results support the findings that drilling activity has only limited impacts on local housing prices.

scholarly journals HEDONIC ANALYSIS OF PRICE IN THE ISTANBUL HOUSING MARKET

2008 ◽  
Vol 12 (2) ◽  
pp. 125-138 ◽  
Author(s):  
Berna Keskin
Keyword(s):  
Housing Prices ◽  
Housing Price ◽  
Urban Housing ◽  
Earthquake Risk ◽  
Hedonic Price ◽  
Modelling Framework ◽  
Price Determinants ◽  
Housing Submarket ◽  
Living Area ◽  
Storey Building

The purpose of this paper is to explore the factors that affect housing prices in Istanbul. A hedonic price model is employed in order to examine housing price determinants with respect to property characteristics, socio‐economic characteristics, neighbourhood quality characteristics, and locational factors. The results reveal that housing prices are affected by these factors: living area size, being in a low storey building, being in a secured site (with swimming pool and garage), and age of the building. In addition to these determinants, the length of time the inhabitants have lived in Istanbul, the average income of the household, neighbour satisfaction and earthquake risk of the area have effects on the residential prices in Istanbul. Further research is suggested by constructing a second model that includes neighbourhood dummy variables as a proxy for submarkets, and a multi‐level modelling framework will be employed in order to analyse the urban housing submarket system. Santrauka Šiame darbe siekiama išnagrinėti veiksnius, kurie daro įtaką būsto kainoms Stambule. Pa si tel kus hedoninį kainų modelį, tyrinėjami būsto kainas lemiantys veiksniai, atsižvelgiant į nekilnojamojo turto charakteristikas, socialinius-ekonominius veiksnius, apylinkių kokybės bruožus ir vietos veiksnius. Rezultatai rodo, kad būsto kainoms įtaką daro tokie veiksniai: gy ve namosios teritorijos dydis, pastato aukštingumas, buvimas sklype ir pastato amžius. Be šių veiksnių, būsto kainas Stambule veikia ir laikas gyventas mieste, vidutinės namų ūkio pajamos, patinkantys kaimynai bei žemės drebėjimų rizika toje terito ri jo je. Siūloma atlikti tolesnius tyrimus, suformuojant antrą fi ktyviuosius apylinkių kintamuosius apimantį modelį, kuris bus taikomas kaip subrinkų pakaitalas, o naudojant daugialypę modeliavimo struktūrą bus siekiama išanalizuoti miesto būsto subrinkos sistemą.

Exploring a multilevel approach with spatial effects to model housing price in San José, Costa Rica

2021 ◽  
pp. 239980832110411
Author(s):  
Eduardo Pérez-Molina
Keyword(s):  
Costa Rica ◽  
Multilevel Models ◽  
Housing Prices ◽  
Housing Price ◽  
Autoregressive Models ◽  
Metropolitan Region ◽  
Spatial Effects ◽  
San Jose ◽  
Conditional Autoregressive ◽  
Conditional Autoregressive Models

A multilevel model of the housing market for San José Metropolitan Region (Costa Rica) was developed, including spatial effects. The model is used to explore two main questions: the extent to which contextual (of the surroundings) and compositional (of the property itself) effects explain variation of housing prices and how does the relation between price and key covariates change with the introduction of multilevel effects. Hierarchical relations (lower level units nested into higher level) were modeled by specifying multilevel models with random intercepts and a conditional autoregressive term to include spatial effects from neighboring units at the higher level (districts). The random intercepts and conditional autoregressive models presented the best fit to the data. Variation at the higher level accounted for 16% of variance in the random intercepts model and 28% in the conditional autoregressive model. The sign and magnitude of regression coefficients proved remarkably stable across model specifications. Travel time to the city center, which presented a non-linear relation to price, was found to be the most important determinant. Multilevel and conditional autoregressive models constituted important improvements in modeling housing price, despite most of the variation still occurring at the lower level, by improving the overall model fit. They were capable of representing the regional structure and of reducing sampling bias in the data. However, the conditional autoregressive specification only represented a limited advance over the random intercepts formulation.

scholarly journals The Impact of Property Tax on the Development of Real Estate Industry

2021 ◽  
Vol 4 (3) ◽  
pp. 73-75
Author(s):  
Ruoke Hu ◽  
Fangke Li
Keyword(s):  
Real Estate ◽  
Rapid Development ◽  
Housing Prices ◽  
Property Tax ◽  
Real Estate Industry ◽  
High Property ◽  
The Real ◽  
The Real Estate ◽  
Significant Growth ◽  
The Impact

In recent years, due to the rapid development of the real estate industry in China, land speculation has begun in addition to the significant growth in economy. However, this rapid development has led to an extreme rise in housing prices, largely owing to high property tax. This article analyzed the impact of property tax on the development of real estate industry and provided countermeasures.

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