Quantile house price indices in Beijing

Zhang, Lei; Yi, Yimin

doi:10.1016/j.regsciurbeco.2017.01.002

Cited by 51 publications

(35 citation statements)

References 31 publications

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“…Dwelling characteristics (Category A) obtained positive values expected in constructed surface area and the number of bathrooms the, same as in other studies [31,38,49,55,57]. Regarding the floor the dwelling was located on, the sign obtained was positive, as in [27,[37][38][39]55,56,60]. On the other hand, negative values were obtained in the age of the property, as occurred in other studies [25][26][27][28][29][30][31]35,36], as well as when the state of housing was to be refurbished [15].…”

Section: Discussionsupporting

confidence: 84%

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Determinants of the Price of Housing in the Province of Alicante (Spain): Analysis Using Quantile Regression

et al. 2019

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After almost a decade of crisis, the housing market in Spain shows significant signs of recovery, with increases in both the average price and the number of sales transactions. Housing is the main asset for the majority of households, and it also has the most resources devoted to it, thus, when it comes to buying a residence, people do not only look at the asset’s intrinsic characteristics, but also consider other particularities such as the neighbourhood, accessibility to services, availability of public transport or adequate funding. The study aimed to analyse and quantify the relationship that exists between the asking price of second-hand housing on the market in Alicante and the attributes that characterise them. This was done using a multivariate analysis to estimate a hedonic pricing model by ordinary least squares and a quantile regression to analyse the impact of the characteristics in different price ranges. The results show the segmentation of the prices in the Alicante market, with higher prices in the northern coastal area over the southern and inland comarcas.

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Section: Discussionsupporting

confidence: 84%

“…Zhang and Yi [60] studied the determinants in the price of residential housing in Beijing during the 2013-2015 period. To this end, they carried out an OLS and a quantile regression with a sample of 190,580 dwellings on a leading real estate portal in China.…”

Section: Pricementioning

confidence: 99%

Determinants of the Price of Housing in the Province of Alicante (Spain): Analysis Using Quantile Regression

et al. 2019

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“…Previous studies have explained that the house price index is derived by the demand factors (e.g., income, trend of labor market, demographic and credit availability) and the supply factors (e.g., Construction cost, land supply index and geographical constraint) (Mohan et al 2019;Li et al 2018;Yan et al 2010;Glindro et al 2011;Craig & Hua 2011). Meanwhile, house prices are also strongly related to other microeconomic house-specific demand factors such as physical, structural, location, environmental and the neighborhood (Zhang & Yi 2017;Stohldreier 2012;Ong & Chang 2013;Tan 2011;Md Yusof 2008). Table 2 below shows the determinants for house price index from selected countries.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This will be more representative of the housing market in the country. A number of studies have considered physical, structural, location and neighborhood factors as being important in determining house prices (NAPIC 2018;Zhang & Yi 2017;Chen et al 2013;Eurostat 2013;Duebel 2012). Meanwhile, Tsai (2012) explains that both demand and supply factors should be incorporated into the computation of the house price index because the HPI is affected by both supply and demand factors.…”

Section: Literature Reviewmentioning

confidence: 99%

Constructing an Enhanced House Price Index Model: Empirical Evidence

Sukrri¹,

Wahab²,

Yusof³

2019

JEM

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The objective of this study is to construct an enhanced house price index model in Malaysia. Having reviewed the current model of the Malaysian House Price Index (MHPI), it is currently found that this index is constructed based on the demand-driven variables. Previous studies explained that both macroeconomic factors (income levels, interest rates, and labor market) and supply factors are included in the construction of the house price index. This study begins by examining the determinants of the existing house price index in Malaysia. This study employs the Autoregressive Distributed Lag Model (ARDL) to discover the short and long-run dynamics between the variables. The study considers the quarterly data from the first quarter of 2008 to the fourth quarter of 2017. The findings reveal that construction cost (CC) and housing loan (HLN) are significant in determining HPI while Overnight Policy Rate (OPR) and land supply (LS) are insignificant with HPI. Then, the housing loan was found to be the most significant variable in determining HPI in Malaysia. Hence, we propose a new enhanced house price index that incorporates new demand and supply variables, by using the Laspeyres approach to calculate the new enhanced HPI. The analysis shows that the enhanced house price index has also recorded the same trend but with a lower value of prices as compared to the current MHPI. This enhanced HPI model may reflect the real situation of the housing market in Malaysia and it is expected to increase the affordability of the society in fulfilling their basic needs. This study may provide evidence for the involved parties to have some policy ramifications to further monitor and take appropriate measures to control the prices of property.

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“…However, this is not an appropriate analysis method for segmented markets, such as low-, mid-, and high-end housing markets [7,24]. Several studies have indicated quantile regression as an alternative to OLS regression, as quantile regression coefficients are estimated differently across the conditional distribution of housing prices [7,19,[25][26][27][28][29][30]. OLS minimizes the sum of squared residuals, whereas quantile regression minimizes the sum of asymmetric weighted absolute values [31].…”

Section: Introductionmentioning

confidence: 99%

The Unequal Impact of Natural Landscape Views on Housing Prices: Applying Visual Perception Model and Quantile Regression to Apartments in Seoul

Lee

2020

Sustainability

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Natural landscape views have positive sides, such as providing restorative effects to urban residents, and negative sides, such as deepening wealth inequality. Previous studies have mainly focused on the positives and rarely on the negatives. From this perspective, this study aimed to analyze the unequal impact of natural landscape views on housing prices for apartments in Seoul. We proposed a visual perception model to analyze natural landscape views and, based on a hedonic price model, we used ordinary least squares and quantile regression to estimate the marginal impacts on housing prices. The results show that: (1) natural landscape views had positive impacts on housing prices, but their impacts did not reach the level of structural and locational characteristics such as apartment area and the distance to subway stations; (2) natural landscape views had different marginal impacts by housing price range and, in particular, had much higher value-added effects on higher-priced apartments, meaning that if old apartment complexes are redeveloped into high-rise ones, the improvement in natural landscape views generates great profit for apartment owners and intensifies wealth inequality; (3) the geographic information system-based visual perception model effectively quantified the natural landscape views of wide areas and is thus applicable for the rigorous analysis of various landscape views.

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Quantile house price indices in Beijing

Cited by 51 publications

References 31 publications

Determinants of the Price of Housing in the Province of Alicante (Spain): Analysis Using Quantile Regression

Determinants of the Price of Housing in the Province of Alicante (Spain): Analysis Using Quantile Regression

Constructing an Enhanced House Price Index Model: Empirical Evidence

The Unequal Impact of Natural Landscape Views on Housing Prices: Applying Visual Perception Model and Quantile Regression to Apartments in Seoul

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