Housing Rental Prices Research Articles

Do landscape amenities impact private housing rental prices? A hierarchical hedonic modeling approach based on semantic and sentimental analysis of online housing advertisements across five Chinese megacities

Real estate premium associated with landscape amenities is a well-studied topic with a primary focus on housing prices. Presumably, the willingness-to-pay for landscape amenities should be very different between homeowners and tenants. Thus far, how landscape amenities affect residential rental prices is not well understood. This paper takes advantage of the big data of online housing advertisements to unravel how landscape amenities are capitalized into rental prices across five Chinese megacities (Beijing, Shanghai, Shenzhen, Hangzhou and Wuhan). Natural language processing, the latent Dirichlet allocation in particular, is first employed to semantically analyze the geo-textual advertisements. It reveals that ‘landscape amenities’ is a typical topic and ‘park’ is a typical component for housing advertisements in the five megacities. The lexicon-based sentimental analysis further shows that the strength of the sentiments associated with the ‘landscape amenities’ varies with cities. A series of hierarchical hedonic models based on the extracted semantic and sentimental aspects are then established for each megacity after segmenting the rental market into submarkets. The capitalization effect of landscape amenities is significant in Beijing, Hangzhou and Wuhan, while it is not significant in Shanghai and Shenzhen. Finally, variance decomposition analysis and marginal implicit price calculation unveil to what extent landscape amenities contribute to residential rental prices. Based on these findings, we discuss several major implications for urban planning. Our study unsettles the popular presumption that landscape amenities are key determinants of real estate values. It renews our understanding of the economic values of landscape amenities theoretically and methodologically.

Unraveling the impact of TOD on housing rental prices and implications on spatial planning: A comparative analysis of five Chinese megacities

Transit Oriented Development (TOD), as a widely practiced planning strategy toward urban sustainability, commonly refers to the integration of transport and land use in the form of an integrated transit station area surrounded by compact urban development and high-quality walkable environment. Although policymakers are very enthusiastic about the potential of TOD in boosting the development of real estate market and livable neighborhoods, how TOD characteristics affect housing rental prices has not well been gauged with empirical data. Taking advantage of online housing platforms, this paper attempts to fill this gap through explicitly unraveling the impact of TOD on housing rental prices across five major Chinese megacities (Beijing, Shanghai, Shenzhen, Hangzhou and Wuhan). We first propose a conceptual framework to explain why and how TOD impacts housing rental prices. TODs are then delineated in the five megacities and T-test confirms that TOD housing properties present significant higher rental prices than non-TOD ones. Spatial hedonic modeling is employed to identify the relationship between TOD characteristics (e.g., metro station, neighborhood and synergy) and housing rental prices in each megacity, and the variable decomposition is further utilized to quantify their relative contribution. Regardless the variations among the five megacities, it is discovered that TOD characteristics generally account for 10%–20% of the housing rental prices and the TOD neighborhood presents the highest relative contribution. The synergy between the metro station and the neighborhood also plays an essential role. Metro station type rather than metro station proximity acts as a significant exploratory variable. These findings foreground that the essence of TOD lies in high-quality urban (neighborhood) development, which are often overlooked in previous studies that overemphasize the importance of “transit” (station) itself. Based on the identified TOD-generated housing rental premiums, we offer five recommendations for TOD practices to be integrated into China's territorial spatial planning. This study renews our understanding of the outcomes of TOD both conceptually and methodologically.

An Assessment of House Loans Cointegration with Macro Variables in Selected Euro Zone Countries

This paper performs an empirical study on house loans, interest rates, unemployment, and house rent prices relationship in Germany, France, Spain and Italy from the year 2003 to 2018. We look for the cointegration and causality relationship between the house loans and macro variables with the help of the Vector error correction model (VECM) and Granger causality methods. We investigate whether variables with monthly data explain better the relationship and causal effects between the variables. We find a long term cointegrating relationship between the real house loans and interest rates, unemployment and house rent prices for France, Spain, and Italy, but not for Germany. On average the equilibrium in house loan development is reached from 4 to 8 years, meaning that long term equilibrium exists, but the variables reach it in a rather long time period. The ECB deposit facility rate included as an exogenous variable in four countries gained no significant power in explaining the short term changes of house loans in any of the country. We reveal a complex interaction between the bank’s credits and unemployment, interest rates, house rental prices in the paper.

The Effect of the Reduction in the Interest Rate Due to COVID-19 on the Transaction Prices and the Rental Prices of the House

Purpose: This study uses 'Autoregressive Integrated Moving Average Model' to predict the impact of a sharp drop in the base rate due to COVID-19 at the present time when government policies for stabilizing house prices are in progress. The purpose of this study is to predict implications for the direction of the government's house policy by predicting changes in house transaction prices and house rental prices after a sharp cut in the base rate. Research design, data, and methodology: The ARIMA intervention model can build a model without additional information with just one time series. Therefore, it is a time-series analysis method frequently used for short-term prediction. After the subprime mortgage, which had shocked since the global financial crisis in April 2007, the bank's interest rate in 2020 is set at a time point close to zero at 0.75%. After that, the model was estimated using the interest rate fluctuations for the Bank of Korea base interest rate, the house transaction price index, and the house rental price index as event variables. Results: In predicting the change in house transaction price due to interest rate intervention, the house transaction price index due to the fall in interest rates was predicted to change after 3 months. As a result, it was 102.47 in April 2020, 102.87 in May 2020, and 103.21 in June 2020. It was expected to rise in the short term. In forecasting the change in house rental price due to interest rate intervention, the house rental price index due to the drop in interest rate was predicted to change after 3 months. As a result, it was 97.76 in April 2020, 97.85 in May 2020, and 97.97 in June 2020. It was expected to rise in the short term. Conclusions: If low interest rates continue to stimulate the contracted economy caused by COVID-19, it seems that there is ample room for house transaction and rental prices to rise amid low growth. Therefore, In order to stabilize the house price due to the low interest rate situation, it is considered that additional measures are needed to suppress speculative demand.

House price index based on online listing information: The case of China

While a timely, accurate house price index with broad coverage is of significant importance in housing market research and analysis, the lack of reliable raw data sources remains a major constraint in the house price index construction in nascent housing markets such as China. In this study, we introduce online listing information as an innovative data source for house price index construction, using China's housing resale markets as an example. Compared with alternative data sources, such as the officially-registered transaction information of housing resales, our analysis shows that online listing data provide a better trade-off between accuracy, reliability, and feasibility, especially after the resolution of potential replicated and/or manipulated data issues using our proposed procedures. Based on the cleaned online listing information, we calculate the first housing resale price indices covering almost all (274) Chinese cities. In particular, for around 200 relatively smaller cities, the index provides the first regular house price indicator, which shows a significant divergence in house price dynamics between different tiers of cities. We also briefly discuss the potential extensions of the listing price index, including the daily house price index and the housing rental price index.

Does the room sharing business model disrupt housing markets? Empirical evidence of Airbnb in Taiwan

Recent research interest has focused on the impact of the room sharing business model on housing markets. However, existing empirical evidence is limited and exclusively focuses on few large cities in the U.S. This study examines the effects of Airbnb on housing rental and sales prices using a unique large-scale dataset comprised of housing market transaction records and the number of Airbnb listings drawn from their website in Taiwan. We estimate a fixed effect model of housing rental and sales price equations and find that a one-standard deviation increase in the number of Airbnb listings raises house rental prices by 0.38%. This finding suggests that a substitution effect is present between Airbnb's short-term accommodation and the housing rental market. Moreover, a larger effect on rental price is found among Airbnb listings that offer an entire room or apartment. Additionally, since September 2017, multinational digital platform companies must comply with a new sales tax policy in Taiwan. We evaluate the effect of this tax policy using the difference-in-difference method and find a negative impact on the number of Airbnb listings and housing rental prices after its implementation. This study is the first to empirically assess the effectiveness of tax policy on regulating room sharing business models.

공공주택 공급 활성화를 위한 프랑스 사회주택의 민간참여 특성 : Entreprise Sociale pour l'Habitat(사회적 주택 기업)을 중심으로

Purpose: This study aims to apprehend the role and importance of private sector in the French public housing policies by focusing on ESH, which supplies and manages about half of French public housing. The participation of private sectors presents increasing importance as a way to stabilize housing market and enhance the sustainability of society. Recently in Korea, since 2015, private sector has started to participate in public housing projects. Therefore it is necessary to observe and analyze the role, importance, the characteristics and institutional and financial system of French private sectors in order to draw implications to Korean public housing policies. Method: The main methods of the study is literature surveys on governmental and institutional reports and analysis of demographic and statistical data such as directory on social rental housing stock. A comparative analysis of public housing projects in Korea and France was also conducted. Result: Korean private sectors needs to enhance public character in term of housing rental price, period and broaden the scope of inhabitants. It is necessary to expand financial support systems such as France’s PEEC. It is also necessary to increase the participation of non-profit private sector through strengthening social economic capabilities.

A social-media-based approach to assessing the effectiveness of equitable housing policy in mitigating education accessibility induced social inequalities in Shanghai, China

Social inequalities induced by education accessibility are widespread and concern land use policy makers globally. Yet much of existing research heavily emphasizes on uncovering the spatial patterns of housing price in relation to education resources. This paper aims to renew our understanding of this classical topic by bringing the importance of temporal and institutional dynamics to the fore. Specifically, this research presents a detailed examination of the relationship between school quality and housing rental dynamics under the intervention of equitable housing policy, with special reference to Shanghai, the most developed megacity in China, also one of the first Chinese cities granting equal rights for renters to access schools within the catchment area. Based on time-series housing rental data collected from social media, the t-test discovers that school quality has a significant impact on housing rental prices after the introduction of this new housing policy. Moreover, housing rental prices within high-quality school districts are significantly higher than those within ordinary school districts. The hedonic model and variance partitioning further confirm that school quality is capitalized into housing rental prices, and its relative contribution varies with temporal periods. More specifically, how the relative contribution of school quality changes over time is consistent with the schedule of primary school enrollment and sensitive to policy intervention. The difference in difference model foregrounds that the equitable housing policy does result in rental prices hike in high-quality school districts, i.e., a 13.5 % rental premium of housings within high-quality school districts compared with those within ordinary ones. We conclude that the equitable housing policy fails to achieve the expected goal and propose alternative suggestions for mitigating social inequalities in education accessibility. This study demonstrates a novel methodological framework for evaluating the social consequence of equitable housing policy based on social media data. It unravels how housing rental changes with school district division and the capitalization effect of school quality in housing rental prices.

Determinants of Residential House Rental Prices in Accra Metropolis

The study analysed the effect of neighbourhood characteristics and structural characteristics on residential rental prices in the Accra metropolis of Ghana. This is in response to the absence of scientific approach to determining rental prices and as a way to influence policies to deal with the arbitrarily fixing of rental prices. Multiple regression analysis was employed in analysing data sourced primarily from tenants of residential houses. The study specified four regression equations, linear, linear-log, log-linear and log-log. The different regression models were found to have different effect on the relationship between house rental prices and its influencing factors. However, the log-log mode and linear-log model revealed similar results, both models revealed that distance from church or mosque to a residential property were significant and positively related to house rental prices whilst all other variables were insignificant. The linear model and log-linear model also showed some similarities. In both models distance to central business district, distance to market, electricity supply, refuse dump, wooded area and urban effect were significant with the same signs in both models. However, single household was significant only in the linear model but not in the log-linear model whereas in the case of the log-linear model variables such as distance to church or mosque, park within fifty metres and uncovered drains were significant but insignificant in the linear model. The study concluded that there is a significant relation between house rental prices and neighbourhood characteristics as well as structural characteristics of a residential property.

The Impact of the Sharing Economy on Housing Rental Prices: The Case of Airbnb in Barcelona

The Impact of the Sharing Economy on Housing Rental Prices: The Case of Airbnb in Barcelona

Does environmental noise affect housing rental prices in developing countries? Evidence from Ecuador

Environmental noise is defined as an unwanted sound which could be generated by anthropogenic activities (for instance industrial or commercial activities), the transit of engine vehicles and melodies at high volume. When these noises are played at the same time and during extended periods, they might cause health issues. As such, excessive environmental noise causes what is known as acoustic pollution. The effects of environmental noise on the housing rental price have been widely studied in developed countries; however, there are no similar studies in developing countries. The main reason for this is the lack of public information about environmental noise levels since the governments of turn have shown little interest in this type of data. On the other hand, measuring the level of noise is a complex and expensive procedure, which has prevented the researchers of the area from studying its effects. For this reason, following the technical recommendations of the Unified Text of the Secondary Legislation of the Ministry of Environment (TULSMA, for its acronym in Spanish), we gathered primary information about the level of environmental noise in Machala, one of the most important cities in Ecuador, which has experienced a great surge of economic growth in the last years. Our results show that per each unitary increment of the level of noise (dB) of the sector where the residence is located, its price diminishes on average in 1.97%. This result is useful for policymakers since the estimated economic value can be used as a reference to undertaking public projects destined to fight this problem. We also evidenced, that sectors with a higher level of noise are at downtown, reaching levels higher than 78 decibels (dB). This result is explained since at downtown the greatest commercial activity of the city is concentrated, as well as a great vehicular flow, where the drivers use excessively the car horn. Furthermore, the situation is aggravated due to the establishment of informal businesses in certain streets, which causes an increment in the noise level. We contribute to the literature evidencing that environmental noise is also a problem for developing countries; in addition, given the lack of information on noise levels in this type of countries, our research provides a clear and precise guide so that future research can raise noise data in other locations of interest.

The cost of floods in developing countries’ megacities: a hedonic price analysis of the Jakarta housing market, Indonesia

Although many megacities in developing countries experience floods annually that affect a large number of people, relatively few empirical studies have evaluated the associated costs. This paper estimates such costs by conducting a hedonic price analysis—providing evidence regarding the impacts of floods on the housing market. A robust regression technique on a simple linear transformation model, and a maximum likelihood estimation technique on the spatial lag version of the simple linear transformation model, are utilised to estimate the correlation between the level of the 2007 floods and monthly housing rental prices in Jakarta, Indonesia. This paper sheds light on the fact that in developing countries’ megacities, the total cost of floods among households is significantly lower compared to the total amount of funding needed to permanently eliminate floods in these megacities. Hence, a constant exposure of the urban areas in developing countries to flood damage will most likely keep happening.

Monitoring housing rental prices based on social media:An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies

National land use policies and strategies worldwide have attempted to establish a healthy housing rental market towards urban sustainability. Monitoring fine-scale housing rental prices should provide essential implications for equitable housing policies. However, doing so remains a challenge because aggregated data were traditionally collected at a coarse scale through census or social surveys. On-line housing rental websites (OHRWs) have become popular social media platforms in the housing studies. This paper attempts to demonstrate how to monitor fine-scale housing rental prices based on OHRWs using the case of Shenzhen in China. Employing hedonic model, a set of housing rental determinants are initially selected from three characteristics (neighborhood, location and structure) and at three levels (nearest accessibility, 15-minute walking distance availability and sub-district availability). Housing rent prediction models are then established (respectively for October 2017 and February 2018) using the training samples collected from the OHRWs and six machine-learning algorithms, including random forest regression (RFR), extra-trees regression (ETR), gradient-boosting regression (GBR), support vector regression (SVR), multi-layer perceptron neural network (MLP-NN) and k-nearest neighbor algorithm (k-NN). Thereafter, the relative importance of the determinants is calculated and visualized using partial dependence plots. Finally, the models are used to monitor housing rental price dynamics for all of the communities within Shenzhen. Results show that all of the algorithms except SVR generally present good performance. Among them, RFR and ETR are the best one in October 2017 and February 2018, respectively. Concerning the spatial pattern of housing rental, the high-high clusters merge in the central districts, whereas the low-low clusters are located in the outskirts, and the growth rate is the greatest in the farthest outskirts from the central districts. Each determinant affects the housing rent across different scale and sub-district availability and nearest accessibility are more important than 15-minute walking distance availability. The two most influential determinants are sub-district job opportunity and nearest accessibility to health care facilities. The case of Shenzhen shows that the demonstrated framework, which integrates machine-learning algorithms and the hedonic modeling, is practical and efficient. The approach is believed to provide an essential tool to inform equitable housing policies.

Labour opportunities provided by Spanish high-speed rail (HSR) commuting services in a period of financial crisis: an approach based on regional wage disparities and housing rental prices

ABSTRACTLabour migration derived from interregional high-speed rail (HSR) commuting is a relatively new behaviour, whose development has partly coincided with a worldwide period of recession. Little research has been done to date to assess the influence of HSR on interregional labour mobility data taking into account key variables linked to a time of financial crisis. Using interregional labour mobility figures (2002–14), a model based on a multivariate panel data analysis was tested to estimate HSR impacts in the three main Spanish interregional commuting relations.

데이터마이닝을 활용한 서울 주요 대학가 주거용 부동산 임대료 모형 수립에 관한 연구

The main motivation of this research is to help university students who are seeking for their residential places, by providing objective information based on data. To this end, we gathered data for a large selection of rental units from Zigbang which is one of the most popular real estate mobile applications in South Korea. Additional information such as distance-to-school-gate which is unavailable from the mobile app was included in our analysis for the purpose of building more accurate models. We employed ridge regression, neural networks, support vector regression, and random forests to model housing rental price based on about 120 thousands observations. The trained models showed the prediction accuracy at around 96%. We also attempted to find out which factors are the most influential in pricing rental fees by analyzing interpretable models.

A New Data Source for House Price Index: The Case of China

While a timely, accurate house price index with broad coverage is of significant importance in housing market research and analysis, the lack of reliable raw data sources remains a major constraint in the house price index construction in nascent housing markets such as China. In this study, we introduce online listing information as an innovative data source for house price index construction, using China’s housing resale markets as an example. Compared with alternative data sources, such as the officially-registered transaction information of housing resales, our analysis shows that online listing data provide a better trade-off between accuracy, reliability, and feasibility, especially after the resolution of potential replicated and/or manipulated data issues using our proposed procedures. Based on the cleaned online listing information, we calculate the first housing resale price indices covering almost all (274) Chinese cities. In particular, for around 200 relatively smaller cities, the index provides the first regular house price indicator, which shows a significant divergence in house price dynamics between different tiers of cities. We also briefly discuss the potential extensions of the listing price index, including the daily house price index and the housing rental price index.

The dynamic effects of subway network expansion on housing rental prices using a repeat sales model

ABSTRACTThis study investigates the effects of subway network expansion on housing rent in existing station areas in Seoul, where subway lines had been added continuously during the sample period from 2000 to 2012. Due to network expansion, some existing stations have been enjoying improvements in network accessibility while others have not. The gap in accessibility improvement between the two groups is causing differentiated levels of capitalization in nearby housing rent. In this study, a modified repeat sales model was developed to detect the inter-temporal changes of rent gradient from the affected subway stations. Based on the model, the time trends of the rent gradients for the two groups were compared. The estimation results show that the marginal value of a proximity of 100 m to a subway station was increased by about 0.6% during the 12 year period. Furthermore, it was found that the marginal value was increased by 0.8% for the more improved group of network accessibility, while only about 0.2% for the less improved. These findings confirm that network expansion of subway lines benefits subway users in existing subway areas as well as those in new station areas, and that those benefits are capitalized into housing rent.

Testing for bubbles in housing markets: some evidence for Brazil

PurposeHousing prices in Brazil have displayed an impressive growth in recent years, raising some concerns about the existence of a bubble in housing markets. In this paper, the authors implement an empirical methodology to identify whether or not there is a bubble in housing markets in Brazil.Design/methodology/approachBased on a theoretical model that establish that, in the absence of a bubble, a long-run equilibrium relationship should be observed between the market price of an asset and its dividends. The authors implement two methodologies. First, the authors assess whether there is a cointegration relationship between housing prices and housing rental prices. Second, the authors test whether the price-to-rent ratio is stationary.FindingsThe authors’ results show that there is evidence of a bubble in housing prices in Brazil. However, given the short span of the data, the authors perform a Monte Carlo simulation and show that the cointegration tests may be biased in small samples. Therefore, the authors should be caution when assessing the results.Research limitations/implicationsThe results obtained from the cointegration analysis can be biased for small samples.Practical implicationsThe information on the excessive increase of the prices of the properties in relation to their fundamental value can help in the decision-making on investment of the economic agents.Social implicationsThese results corroborate the hypothesis that Brazil has an excessive appreciation in housing prices, and, as Silva and Besarria (2018) have suggested, this behavior explains, in part, the fact that the central bank has taken this issue into account when deciding about the stance of monetary policy of Brazil.Originality/valueThe originality is linked to the use of the Gregory-Hansen method of cointegration in the identification of bubbles and discussion of the limitations of the research through Monte Carlo simulation.

Impacts of Haze on Housing Prices: An Empirical Analysis Based on Data from Chengdu (China).

Based on cross-section data of 20 districts in Chengdu, this article reviews the relationships between haze and housing prices with the combined application of Spatial Error Model (SEM) and Spatial Lag Model (SLM). The results illustrate that haze significantly have negative impacts on both the selling and rental prices of houses. Controlling other variables, if the air quality index rises by 0.1, the housing selling prices and rental prices will drop by 3.97% and 4.01%, respectively. Interestingly, housing rental prices have a more significant response to the air quality than housing sale prices. Residents are willing to pay a premium for better air quality and the influence of air quality is partially reflected in housing prices, which indicates that better air quality has been becoming a scarce resource with the improvement of people’s living standard. Furthermore, the impacts of haze on housing prices are also expected to lead to a “crowding out effect” in different regions. This would be detrimental for human capital accumulation and will accelerate the regional divergence in the internal economy and population structure, thus forming a region “fence” within cities.

Exploring the association of urban or rural county status and environmental, nutrition- and lifestyle-related resources with the efficacy of SNAP-Ed (Supplemental Nutrition Assistance Program-Education) to improve food security.

To investigate the association of policy, systems and environmental factors with improvement in household food security among low-income Indiana households with children after a Supplemental Nutrition Assistance Program-Education (SNAP-Ed) direct nutrition education intervention. Household food security scores measured by the eighteen-item US Household Food Security Survey Module in a longitudinal randomized and controlled SNAP-Ed intervention study conducted from August 2013 to April 2015 were the response variable. Metrics to quantify environmental factors including classification of urban or rural county status; the number of SNAP-authorized stores, food pantries and recreational facilities; average fair market housing rental price; and natural amenity rank were collected from government websites and data sets covering the years 2012-2016 and used as covariates in mixed multiple linear regression modelling. Thirty-seven Indiana counties, USA, 2012-2016. SNAP-Ed eligible adults from households with children (n 328). None of the environmental factors investigated were significantly associated with changes in household food security in this exploratory study. SNAP-Ed improves food security regardless of urban or rural location or the environmental factors investigated. Expansion of SNAP-Ed in rural areas may support food access among the low-income population and reduce the prevalence of food insecurity in rural compared with urban areas. Further investigation into policy, systems and environmental factors of the Social Ecological Model are warranted to better understand their relationship with direct SNAP-Ed and their impact on diet-related behaviours and food security.