House Sales Research Articles

The effect of the COVID-19 pandemic on TOM in the housing market

Purpose The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations. Design/methodology/approach Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present. Findings This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated. Originality/value This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

Liability of Housing Developers and Residents Relating to Environmental Management Dues

The purpose of this study is to investigate the legal relationship that occurs between developers and housing residents during the process of buying and selling houses starting from the binding agreement for the sale and purchase of houses until the house is occupied. Furthermore, this study discusses the liability of developers and housing residents in the event of default or unlawful acts. The policy of shutting down clean water supply carried out by developers to residents is often not only related to the failure to pay Environmental Management Dues (IPL) bills which are combined with water bills, but also often associated with other things as an effort to suppress housing residents. From a legal perspective, the developer's actions can be categorized as unlawful. On the other hand, housing residents who are dissatisfied with the developer's services choose not to pay (IPL), where from the developer's perspective, the resident's actions are in default. This study will examine legal relations and legal resolution solutions related to conflicts over Environmental Management Dues between Developers and Housing Residents.

The non-linear dynamics of South Australian regional housing markets: A machine learning approach

Traditional linear models often struggle to capture regional housing markets' complex, non-linear dynamics. This study addresses this gap by developing and applying advanced machine learning algorithms to unlock unique insights into South Australian housing price behavior. Leveraging a comprehensive dataset of over 10,000 regional house sales from 2010 to 2021, we explore the non-linear relationships between housing prices and microeconomic factors (e.g., house size, land area, building quality) and socioeconomic characteristics (e.g., proximity to amenities and income levels). Our analysis employs a multi-step approach, including feature engineering, spatial data integration, correlation tests, multilevel modeling, and non-linear machine learning algorithms including Decision Tree, Random Forest, Gradient-Boosted Tree, and Artificial Neural Network. The key finding is that machine learning models outperform traditional econometric models in predicting regional housing prices, with higher accuracy and greater goodness of fit. Furthermore, we identify specific non-linear relationships, such as the increasing marginal impact of proximity to the sea on house prices as distance decreases. These findings offer valuable insights for policymakers, real estate professionals, and stakeholders, informing regional planning, infrastructure provision, and economic development strategies. This study sheds light on the complex dynamics of South Australian housing markets and lays the foundation for further research.

A Study on the Supply of Private-Sale Apartments in Public Housing Districts and the Subject of Administrative Charges

Since the first general law on rental housing—a type of public housing—was implemented in 1985, the scope of public housing has now expanded beyond rental housing to pre-sale housing, which was mandated by the Bogeumjari Housing Act in 2009. To supply public housing on a timely and large scale, public housing districts must be established first, which is the driving force behind the possibility of land acceptance according to public needs. Focusing on the Bogeumjari Housing Act, which first introduced the concept of public housing for sale, we examine the creation and supply of housing districts as well as the construction and supply procedures of housing and compare them with the Housing Site Development Promotion Act and the Housing Act applied to private housing. After the public housing district was established by the acceptance method, in addition to the construction of public housing in the district, a path for private housing could be developed by selling some of the district’s land to the private sector within the current legal framework. However, creating land for the public interest and then selling it to the private sector can result in the same problems as those raised in the Housing Site Development Promotion Act. The Housing Site Development Promotion Act conducted land expropriation for the large-scale new city development; however, it gradually became powerless against opposition from landowners owing to the cultivation of a sense of rights. The Roost Housing Act can be viewed as a means to bypass these problems. When a large-scale housing district is created for housing construction, measures to improve wide-area transportation must be implemented, and appropriate wide-area transportation facility charges may be imposed. At this point, it may be unclear whether the levy is imposed at the formation of a housing district or at the housing construction stage. As the Bogeumjari Housing Act focuses on the creation of a district, imposing a levy during the formation of the housing district may be more justified from the perspective of a housing site development plan.

Ecosystem service values provided by National Parks to residential property owners

This paper focuses on the ecosystem service benefits to homeowners from living in close proximity to three National Parks. Knowing these ecosystem service benefits is of policy and management relevance as expansion of existing Parks and protected areas along ecosystem boundaries often requires acquisition of undeveloped private land or other multiple use public lands, something that local officials may oppose. While the effects on house prices from urban parks, and two other types of U.S. Federal lands have been studied, the effect of lands in the U.S. National Park System on house prices had not been studied at the time of this research. To fill this information gap, hedonic price models were estimated to calculate the effect on residential house prices of proximity to three National Parks (NPs). After controlling for other characteristics of the house, neighborhood, and several other types of open space (e.g., National Forests, County Parks), we estimate house sale prices were on average 9.8% higher for houses within 2 km of National Park boundaries. This house price premium represents the value of the ecosystem services received by homeowners from living in close to these NPs. Quantifiable information on local ecosystem service benefits may ameliorate some concerns by local governments about converting private land or other agencies' multiple use lands in order to expand NPs or protected areas to better reflect ecosystem boundaries rather than historical, and often arbitrary boundaries. In addition, quantifiable information on enhancement of property values may broaden stakeholder groups to include the real estate industry. Thus, our results may support agencies in better achieving their objectives of managing NPs as ecological units.

The Impact of Real Estate Purchase Restriction Policy on Urban Economic Development—A Case Study of Beijing

Since 2010, Beijing's purchase restriction policy has been adjusted several times to adapt to the market and promote stability. In April 2024, the policy was eased for housing areas outside the Fifth Ring Road, marking the first major adjustment in 13 years. This paper analyses the economic impact of Beijing's real estate purchase restriction policy, focusing on its effect on investment, consumption, industrial structure, and employment. The study indicates that the purchase restriction policy initially hinders investment and consumption in the short term but contributes to market stability and industrial optimization in the long run. The policy change has increased housing sales and growth in related industries outside the Fifth Ring Road, promoting economic development in the suburbs. Furthermore, the policy has a nuanced impact on the employment market, encouraging labor mobility toward high-tech and service industries while affecting employment in real estate-related sectors. The study suggests that policymakers need to consider the impacts of various aspects to ensure the healthy development of the real estate and employment markets.

Macroeconomic drivers effect on housing sale prices in China

Purpose The purpose of this study is to conduct a thorough empirical investigation of the intricate relationship between urban housing sales prices and land supply prices in China, with the aim of elucidating the underlying economic principles governing this dynamic interplay. Design/methodology/approach Using monthly data of China, the authors use the asymmetry nonlinear autoregressive distributed lag (NARDL) model to test for nonlinearity in the relationship between land supply price and urban housing prices. Findings The empirical results confirm the existence of an asymmetric relationship between land supply price and urban housing prices. The authors find that land supply price has a positive and statistically significant impact on urban housing prices when land supply is increasing. Policymakers should strive to strike a balance between safeguarding residents’ housing rights and maintaining market stability. Research limitations/implications Although the asymmetric effect of land supply price has been identified as a significant contributor in this study, it is important to note that the research primarily relies on time series data and focuses on analysis at the national level. Although time series data offer a macroscopic perspective of overall trends within a country, they fail to adequately showcase the structural variations among different cities. Practical implications To ensure a stable housing market and meet residents’ housing needs, policymakers must reexamine current land policies. Solely relying on restricting land supply to control housing prices may yield counterproductive results. Instead, increasing land supply could be a more viable option. By rationally adjusting land supply prices, the government can not only mitigate excessive growth in housing prices but also foster the healthy development of the housing market. Originality/value First, the authors have comprehensively evaluated the impact of land supply prices in China on urban housing sales prices, examining whether they play a facilitating or mitigating role in the fluctuation of these prices. Second, departing from traditional linear analytical frameworks, the authors have explored the possibility of a nonlinear relationship existing between land supply prices and urban housing sales prices in China. Finally, using an advanced NARDL model, the authors have delved deeper into the asymmetric effects of land supply prices on urban housing sales prices in China.

THE INS AND OUTS OF SELLING HOUSES: UNDERSTANDING HOUSING‐MARKET VOLATILITY

AbstractThis article documents the role of inflows (new listings) and outflows (sales) in explaining the volatility and comovement of housing‐market variables. An “ins versus outs” decomposition shows that both flows are quantitatively important for housing‐market volatility. The correlations between sales, prices, new listings, and time‐to‐sell are stable over time, whereas the signs of their correlations with houses for sale are found to be time‐varying. A calibrated search‐and‐matching model with endogenous inflows and outflows and shocks to housing demand matches many of the stable correlations and predicts that the correlations with houses for sale depend on the source and persistence of shocks.

딥러닝을 활용한 서울시 주택 유형별 전세가격에 영향을 미치는 요인분석

1. CONTENTS
 (1) RESEARCH OBJECTIVES This study used deep learning DNN model to empirically analyze the factors affecting Chonsei prices of each housing type in Seoul.
 (2) RESEARCH METHOD The output variables are set as the apartment Chonsei price index, the single-family home Chonsei price index, and the townhouse Chonsei price index, and the input variables are the apartment sales price index, single-family home sales price index, townhouse sales price index, number of employed people, and corporate bond yield. housing construction permit records. The spatial scope was set to Seoul, and the temporal scope was set from January 2007 to October 2023.
 (3) RESEARCH FINDINGS Looking at the LIME(Local Interpretable Model-agnostic Explanations) of each Chonsei prices input variable by housing type, when the apartment sale price index is -0.66 or less, the single-family house sale price index is in the range of -0.66 to -0.38, and the townhouse sale price index is -0.59 or less. it was found to have a negative impact on the Chonsei price index for each housing type. When the corporate bond yield was in the range of -0.89 to -0.23 for apartments, -0.89 to -0.23 for single-family homes, and -0.89 to -0.23 for townhouses, it was found to have a positive (+) impact on the Chonsei prices index for each housing type.
 2. RESULTS
 The implications are that although there are differences by housing type, the Chonsei price appears to be greatly influenced by the sale price and interest rates. Therefore, the government must continuously monitor the Chonsei price and sale price by housing type to ensure housing stability for the working class and middle class and appropriate liquidity management.

KONUT İŞLEM HACİMLERİ ARACILIĞIYLA KONUT PİYASASININ MEKANSAL ANALİZİ: MENTEŞE, MUĞLA

As there is a reciprocal relationship between the patterns of investor decisions, local and central government strategies, household mobility, and housing transactions have repercussions on both macro and micro scales. Housing transactions within a metropolitan region can alter certain neighbourhood dynamics through the cycle of housing sales regarding urban development patterns, socio-economic characteristics of the residents, tenure type, neighbourhood, and housing quality. Even though the connection between neighbourhood dynamics and housing transactions is undeniable for spatial disci-plines, there are very limited studies for that matter. This study aims to explore the housing market dynamics of Menteşe via housing transactions. It employs three different datasets (Open Street Map (i), Household, housing and office data collected by the Turkish Statistical Institute (ii), Title deed transaction statistics by The Directorate of Land Registry and Cadastre (iii)). For spatial heterogeneity, Moran’s I, and spatial randomness, a local indicator of spatial association (LISA) has been applied. The findings revealed the role of centrality and the importance of mixed land use in housing markets even though all the neighborhoods on the periphery are very close to the city center in terms of distance.

The Economic Effects of Stormwater Best Management Practices (BMPs) on Housing Sale Prices in Washington, D.C.

This study aims to investigate the economic effects of stormwater best management practices (BMPs) on housing sale prices in Washington, D.C., BMPs play a significant role in mitigating multiple threats, such as water pollution, soil erosion, and property damage. While studies on the economic value of BMPs were limited, literature addresses that housing sale prices can be affected by nearby stormwater BMPs. This study addresses the following research questions: Do stormwater BMPs positively impact housing sale prices? How do proximity and number of structural BMPs affect the housing sale prices? We used the hedonic pricing method by applying multiple linear regression models to determine whether a set of independent variables significantly improved the models. Our primary findings indicate that BMPs have positive, negative, or no effects on housing sale prices. The proximity of BMPs inside of parks increased housing sale prices in all buffers. In contrast, the proximity of BMPs outside of parks and impervious roads decreased housing sale prices in all buffers. Percent tree canopy coverage negatively linked to a 50 m buffer and had no relationship with other buffers on housing sale prices. This study implies that BMPs impact housing prices and can be improved by landscape architects, policymakers, and stakeholders.

Are gated communities “safe havens”? Examining housing price dynamics of Chinese gated and non-gated communities during COVID-19 pandemic

Gated communities (GCs) and associated socio-economic implications are of global concern. How GCs and non-GCs responded to the external shock of the pandemic has not been comprehensively understood. Drawing on the information of infected cases during the first wave pandemic (January-March 2020) and longitudinal data of housing sales and rental prices for each community from July/2019-June/2020 in 11 Chinese cities, we employ a hedonic difference-in-differences approach accounting for heterogenous effects in stagger timing to discover the effects of COVID-19 on housing price differences between GCs and non-GCs. Moreover, the buffer zones of infected epicentres are delineated to explore the spillover effects. Compared with GCs, non-GCs with different distances from the pandemic epicentres experienced significantly greater reductions in both housing sales and rental prices at varying degrees. While the effect on housing sales prices was significant over time, it only lasted for one month for rental prices. This study enhances our understanding of housing price dynamics and the socio-spatial implications of GCs.

Analisis Kesantunan Berbahasa Dalam Film Pendek Negosiasi Kelas X Pada Saluran Youtube Cinta Bahasa

The analysis of linguistic politeness in a short film about negotiations for class X on the Cinta Bahasa YouTube channel focuses on the analysis of politeness in negotiation activities. The purpose of analyzing linguistic politeness in the short film about class X negotiations is to find, describe, and evaluate the mistake in the dialogues in the videos titled "Short Film on Shop Rental Negotiations", "Land Sale and Purchase Negotiations", "Shop Sale and Purchase Negotiations", "House Sale and Purchase Negotiations", "Short Drama on Renting Rice Fields", "Video of Osim Meeting Negotiations", "Negotiations for the Preparation of the Indonesian Independence Day Ceremony" , and “Negotiation of Buying a Cell Phone". This research uses two research methods: a descriptive method with a qualitative approach and a theoretical method with a pragmatic approach. The data analyzed was collected using observation and note-taking techniques. The results of the data analysis are presented in the form of descriptions along with explanations for each linguistic politeness found in the videos. Ten maxims were found: the maxim of wisdom, the maxim of generosity, the maxim of praise, the maxim of humility, the maxim of agreement, the maxim of sympathy, the maxim of apology, the maxim of forgiveness, the maxim of emotion, the maxim of opinion, and the maxim of silence. This research provides an explanation of the use of linguistic politeness in the negotiation process. The language politeness that is most dominantly used on the Cinta Bahasa Youtube channel is compliance with the maxim of agreement and violation of the maxim of humilty. The benefit of this analysis is to provide an understanding of the appropriate use of language in accordance with language norms in the negotiation process, and readers can understand and use it in the context of good language as a result of this analysis.

Institutions, urban space, and residential markets in globalizing Shanghai: A comparative study of housing sale and rental prices

ABSTRACT China’s skyrocketing residential market has raised concerns about housing affordability in metropolitan areas. Current literature usually overlooks the various mechanisms between sale and rent, which may unfold more fundamental structural forces beyond housing location and attributes. This study constructs an analytical framework to investigate how global, regional, and local development contexts, including institutional forces, urban spatial structure, human capital, and neoclassical factors, affect apartment sales and rental prices in Shanghai, China. These factors collectively shape the city’s residential market, but renters and buyers may show different housing preferences. Such variation is highly related to their residential status, as renters may prioritize job opportunities while buyers value service amenities more. From the spatial perspective, suburban areas are more inclusive than urban centers, as globalization and institutional factors have exacerbated inequality in the residential housing market in urban centers, and homeownership is changing the patterns of segregation in Chinese cities.

МОДЕЛЮВАННЯ РЕЗУЛЬТАТІВ ФУНКЦІОНУВАННЯ ПІДПРИЄМСТВ, ДІЯЛЬНІСТЬ ЯКИХ ПОВ’ЯЗАНА З ДЕВЕЛОПМЕНТОМ

The article is devoted to the construction of a multifactorial economic-mathematical model of the dependence of the results of the functioning of enterprises of activities related to real estate development on the main macroeconomic factors. The relevance of studying the influence of factors influencing the results of the functioning of enterprises operating on the real estate market is substantiated. The relevance of the practical application of economic-mathematical modeling tools, in particular in areas related to the functioning of the real estate market, is highlighted. It is proposed to use the tools of economic and mathematical modeling to formalize the influence of individual factors on the results of the functioning of enterprises actively working in the real estate market (construction enterprises and enterprises carrying out transactions with real estate). The dynamics of changes in the net profit (loss) of enterprises by types of economic activity "Construction" and "Operations with real estate" were analyzed. Based on the results of the correlation analysis, the factors included in the dependence model were determined. The expediency of building a dependence model on the basis of a smoothed series of initial data, constructed using a three-point moving average, has been proven, which allows to eliminate the influence of seasonality on the studied phenomena. A model of the dependence of the results of the functioning of enterprises of types of activities related to real estate development on changes in the number of certified contracts for the purchase and sale of housing, the index of construction products and the index of housing prices was built. The model was checked for adequacy and reliability using generally accepted criteria. The application of the built model will allow further forecasting at the macro level the general financial results of enterprises working in the field of construction and real estate operations. Such forecasting can provide guidelines for the further development of the real estate market, aimed at improving the efficiency of the functioning of builders and developers.

Evolution of Home Ownership of Households in Turkey

Home ownership rate has been declining steadily since 2004. This decrease is remarkable during a period when the construction sector is growing and that growth is reflected to the house sales. In this study, the reasons behind the decline in the home ownership rate are examined with descriptive statistics and econometric analysis using data from Household Budget Survey. The results show that the probability of home ownership of households is positively related to income level and age and negatively related to price-income ratio, price-rent ratio as well as female household head. On the other hand, when the responsiveness of home ownership to interest rate is analyzed by group of income levels of households, it is observed that households in the upper income level quintile are more responsive to interest rates than those in the lower income level quintile. This indicates that some of the house purchases may be made by the upper income level group for investment purposes and that households in low-middle income level quintiles are not sensitive to interest rates. In other words, the surge observed in house sales in recent years cannot be observed in home ownership rates. Therefore, it is believed that policies such as increasing the income assurance and public housing projects where the down payment and monthly payments are kept low, will be beneficial in order to increase the home ownership of low and middle income groups of households.

La vivienda en Las Palmas en el seiscientos: presión social, viviendas y evolución de los precios

Housing was a major economic factor during the Modern period, with social particularities as significant as those that exist today. In the modern urban world, even reduced in its economic contribution compared to the obvious weight of the rural, social groups lived together in constricted spaces, often overcrowded and, in most cases, unhealthy, delimited by the orography, agricultural plots urban areas or defensive walls. The layout of the city, the location of the main administrative and ecclesiastical buildings, the accessibility to buildable land, the functions developed or the demographic growth of each city generated different responses in their housing sales and rental markets. Speculation, the expulsion of economically less favored groups, the exclusivity of certain areas or the deterioration of others was a constant in modern cities, Las Palmas being no exception.

Antiquity market trends in Anatolian figurines 1999–2022: Studies in price, prevalence and provenance

AbstractIn recent decades, the Turkish government has adopted a proactive policy of pursuing the restitution of cultural objects it believes were illegally removed from its territory. Utilising open-access sale data, the following article examines the impact of this formal restitution policy upon antiquity market sales in Anatolian figurines between 1999 and 2022 across three major international auction houses and the internet market. By casting a forensic gaze upon seemingly decreasing sale rates and ostensibly improving standards of provenance, this study suggests that apparent improvements in the market for Anatolian figurines should not be attributed to the Turkish government restitution policy. Instead, it argues that salient commercial dynamics and the profit-oriented business strategies of individual auction houses are the operative factors in shaping antiquity market data. In doing so, it provides the first quantitative market analysis of auction house sales in Anatolian figurines, widens the applicability of existing methodologies for navigating the duplicitous nature of antiquity market data, and offers much needed empirical insights into the illicit antiquities trade.

Implementasi Framework Streamlit Sebagai Prediksi Harga Jual Rumah Dengan Linear Regresi

This research aims to develop an Artificial Intelligence (AI)-based application using the Streamlit framework to predict house sale prices in Banjarmasin City using Linear Regression methodology. The increase in demand and supply of properties in Banjarmasin City poses a complex challenge in determining house sale prices. The Linear Regression method was chosen as the primary analytical tool to identify factors influencing house sale prices. This application utilizes historical data of house sale prices and variables such as land area, building area, number of rooms, proximity to public facilities, and geographical location as inputs for the Linear Regression model. Furthermore, the Streamlit framework is employed to create an interactive and user-friendly interface for end-users. The outcome of this research is an AI application that assists potential buyers or sellers in Banjarmasin City in determining competitive prices. By inputting information about the property being evaluated, users can obtain a more accurate estimated sale price based on factors identified by the Linear Regression model. In testing the application, actual house sale price data from Banjarmasin City was used to assess the model's accuracy. The testing results indicate that the application is capable of providing reasonably accurate price estimates, achieving an accuracy level of 67.8%. Thus, this AI application holds the potential to be a valuable tool in the property industry in Banjarmasin City, aiding stakeholders in more informed and data-driven decision-making regarding house sale prices. Additionally, this application could serve as a foundation for further developments in AI research and property price analysis.

House Price Prediction Using Data Mining with Linear Regression and Neural Network Algorithms

The need for housing in big cities is very high because most offices and economic centers are in big cities. Limited land and high demand cause house prices to rise. Many developers build housing on the outskirts of big cities with access to trains and toll roads to make transportation easier. Property developers compete by providing the best prices, various choices of house specifications, ease of the mortgage process, and attractive promotions such as no down payment. A house is a long-term investment whose price increases yearly, so proper analysis is needed to buy a place to live in. Several factors influence the price of a house, including location, land area, building area, building type, and so on. This research aims to create a house price prediction model using the Linear Regression Algorithm and Neural Network so that the results can be useful for property agents in predicting house sales or from the buyer's side in predicting house prices. The results of this research use the Linear Regression Algorithm RMSE 0.775, while the Neural Network Algorithm uses RMSE 0.645. From this research, modeling using the Linear Regression Algorithm has better results. Still, the Linear Regression Algorithm and Neural Network Algorithm have RMSE results that are close to accurate and have small errors.