Property Prices Research Articles

The Impact of Unnatural Death Stigmatization on Neighboring Property Prices: Propensity Score Matching and Difference-In-Differences Analysis.

This study primarily investigated the impacts of unnatural in-house deaths on nearby property prices. Propensity score matching was used to assess the homogeneity of experimental and control groups, and the difference-in-differences (DID) method was used for their comparison. Data for real estate transactions in Taipei City from 2012 to 2022 were retrieved from the Ministry of the Interior's real estate actual transaction price registration query system. The DID interaction variable coefficient of 0.066 was significant at the 10% level. A comparison of the experimental and control groups showed a 6.8% property price difference following an unnatural in-house death. This suggests that an unnatural in-house death has a positive impact on property prices nearby. There were four unnatural deaths at the Xining Public Housing from 2003 to 2012 and four at the Jinxing Building from 1984 to 2012. The high incidence of events and the disclosure of information may have depreciated nearby property prices. The study results show that the stigmatization of unnatural in-house deaths reduces nearby property prices. The depreciation of implicit prices reverted and followed a U-shape pattern after several years.

The effect of subsidized flood insurance on real estate markets

AbstractSubsidized insurance against extreme weather events improves affordability among households in at‐risk areas but it can weaken the risk signal via property prices. Leveraging a granular data set of all property transactions and flood events in England, we study the effects of a reinsurance scheme that lowers insurance premiums for at‐risk properties. We document that the introduction of this scheme increases prices and transaction volumes of flood‐prone properties. This fully offsets the negative direct effects of flooding on property prices, with high‐income areas and high‐value properties benefiting relatively more. Our findings speak to the debate on climate adaptation policies and their consequences for wealth distribution.

Observing the interaction between energy generation carbon emissions and economic conditions could consider generation, intensity, and times of the day.

Management of carbon emissions of regional electricity generation involves resolving much nuanced relationships. However, current studies pertaining to the relationship between energy and the economy utilize only quarterly and annual data and focus on the long-run relationship but are oblivious to temporal associations. This study takes the context of the State of Texas and utilizes per 15min of data between 2007 and 2020 to derive the monthly time series of life-cycle emissions, emissions factor, and energy generation. The study also divides each day into three portions (0:00-8:00, 8:00-16:00, and 16:00-24:00) in order to descend precision down to the hourly level. Impulse-response analysis indicates that (1) employment conditions impact emissions by affecting the amount of electric energy generated, while demographic (population as reflected by property price) tends to impact emissions by affecting the carbon intensity of electricity, and (2) the emissions produced during 0:00-8:00 display different reaction to fluctuation of regional economic conditions compared to emissions produced during other periods, the effect emanates from either energy generation fluctuation or emissions factor shocks. The broader implication of the findings lies in an emphasis on time-interval-specific analysis and concurrent consideration of both energy generation and emissions factors that can lead to optimized policy implementation results.

An investigation of the impact of energy performance certificate (EPC) ratings on residential property prices in Oxfordshire: a hedonic study

ABSTRACT This paper investigates whether energy performance ratings measured by Energy Efficiency Certificates (EPCs) affect the sales prices of residential properties in Oxfordshire, UK. The study makes use of 186,913 sales transactions that took place between 1995 and 2023. Using a hedonic regression estimation, a nonlinear pattern between energy efficiency levels and the sales prices of dwellings, are observed. The results also show that there is evidence of a significant positive relationship between EPC ratings and detached, semi-detached, terraced houses and flats. In the second part of this study, we examine the effects of age, county and type of dwelling on price premiums. To the best of the authors’ knowledge, this study is the only study, using the most comprehensive literature; and the largest, most up to date available data in analysing the Oxfordshire real-estate market, in terms of energy efficiency and price premiums.

Do affluent neighbourhoods pay more for transit access? Exploring the capitalization of employment accessibility within different housing submarkets in Vancouver

Decades of research indicate that accessibility plays a fundamental role in the urban systems of cities by influencing land markets and household location choices. Accessibility is also often positioned as a policy tool in enhancing the well-being of disadvantaged population groups. Considered together, recent research into transportation equity underscores the need to critically investigate the distribution of accessibility with their affordability impacts. To better understand this dynamic, this research assesses variability in the relationship between employment access and house prices within different neighbourhood types in Metro Vancouver. We first calculate network accessibility to employment opportunities. Next, to integrate equity, we use sociodemographic indicators from census data to establish a typology of four neighbourhoods: Affluent, Stable Middle-class, At-Risk Middle-class and Economically-disadvantaged. Finally, using real estate data, we employ spatial econometric models to estimate differences in the capitalization of accessibility in residential property prices across these neighbourhood types. Results suggest that the value of transit access does differ by neighbourhood type in Metro Vancouver. While more affluent neighbourhoods exhibit the highest marginal willingness to pay for accessibility, predicted prices are highest in disadvantaged neighbourhoods due to their higher absolute levels of access. The research offers new insight into how property price effects vary according to different accessibility and sociodemographic contexts, and highlights important implications for both policy and future research.

The effect of flood risk on house prices in the Basque Country

We analyze property prices, including both sales and rents, in relation to climate change-associated flood risk arising from sea level rise and river overflow. Our body of evidence consists of sale and rent offered prices in the Basque Country from 2007 to 2017, totaling over 329,000 observations. Our quantitative methods depart from those in other studies by using the ratio of sale to rent prices, deemed more likely to reflect the effects we are investigating. We use geographically weighted regression (GWR) with neighborhoods tailored to risk areas. Our findings provide some evidence of risk capitalization, although limited to areas of higher risk.

The Effect of War on Spatial Differentiation of Real Estate Values and Urban Disorder in the Damascus Metropolitan Area

The Syrian war, which commenced in 2011, transformed the Damascus real estate market due to heightened insecurity and tenure disputes. Using the hedonic price models, including 2411 housing transactions over the period 2010–2022, this study aims to understand the spatial dynamics of the real estate market in wartime. Our findings indicate that war variables have had a significant impact on the differentiation of property prices. Notably, property attributes have a more substantial impact on real estate values than district location, with severely damaged buildings in Damascus City resulting in an 89% decline in prices, while prices in Rural Damascus districts have decreased by 50%. Additionally, this study examines the urban texture of Damascus using correlation and homogeneity statistics derived from the gray-level co-occurrence matrix obtained from Google Earth Engine. Our findings show that correlations were highly differentiated, particularly among Rural Damascus districts, with a total decline of 87.2%. While homogeneity values decreased overall between 2015 and 2019, they improved slightly after 2019. This study guides decision makers in mitigating severe property value variations across war-affected urban areas by fostering spatial justice in property rights and promoting balanced investment and sustainable real estate development during the post-war recovery phase.

Determining the Influence of Real Estate Features on Prices with Partial Dependence Plots: A Case Study in Szczecin, Poland

Abstract The study explores the application of Partial Dependence Plots (PDP) in the analysis of real estate features. The study centers on a selected real estate market in Szczecin, Poland, aiming to highlight the efficacy of PDP in understanding and interpreting the complex relationships between various features and property prices. The primary objective is to showcase the potential of PDP in capturing the nuanced interactions between real estate attributes and their impact on market prices. The CatBoost model, known for its robust handling of categorical features and strong predictive capabilities, is employed as the machine learning algorithm for this analysis. The performance of this model will be compared against a traditional multiple linear regression model, providing insights into the advantages of leveraging advanced machine learning techniques in real estate analysis. Results obtained from the analysis will be presented and discussed, shedding light on the interpretability and accuracy of the CatBoost model compared to the traditional linear regression approach. The presentation will conclude with implications for real estate practitioners and researchers, emphasizing the potential for PDP to enhance the transparency and understanding of complex models in the real estate domain. This research contributes to the growing body of knowledge on the application of advanced machine learning techniques in real estate analysis.

Analysis Of the Impact of China's Population Age Structure on Residential Commercial Housing Prices

With the intensification of global aging, the impact of changes in population age structure on the real estate market is becoming increasingly significant. This article aims to explore the impact of changes in China's population age structure on the prices of residential commercial housing, to provide a scientific basis for regulating the real estate market. By analyzing data from 30 provinces and cities in China from 2013 to 2022, this article finds that as aging population intensifies and the number of children decreases, both the old-age dependency ratio and the child-age dependency ratio have a negative impact on housing prices. This means that the aging society and the economic pressure of childcare may weaken the demand for home purchases. Household Saving Rate and increase in total population positively drive house prices, while the relationship between per capita income and house prices is not significant. From a regional perspective, the aging population in the eastern region has a significant effect on suppressing housing prices, while the impact is gradually evident in the central region. The western region has a smaller impact and population growth suppresses housing price increases. The study emphasizes the impact of changes in the age structure of the Chinese population on the prices of residential properties and proposes policy recommendations such as improving old-age security and optimizing the housing supply structure to promote the steady development of the real estate market.

Does flood risk affect property prices? Evidence from a property-level flood score

One in six properties in England is exposed to flood risk and around half of those affected properties can be characterised as high risk. In this paper we examine whether the probability of flooding is capitalised in England's property market prices. We use unique property-level data from Rightmove, UK's no.1 property website and the property-level FloodScoreTMby Twinn, Royal HaskoningDHV. The latter is a metric of objective flood risk based on the likelihood of an individual property being flooded due to rainfall, overflowing rivers and tidal surges and is commonly used by UK lenders. By comparing the unconditional averages of our data, we find that properties at risk are sold at an 8.14 % discount compared to non-exposed properties, and the price discount increases to 32.2 % for properties with very high flood risk. By 2080 the flood events are expected to become more frequent and the average flood risk is projected to increase by 8 %. Our empirical model suggests that one percentage point increase in properties' flood risk is associated with a decline of 0.07 % to 0.11 % in both sold and asking property prices. The impact is higher for properties of which flood risk is expected to increase or for regions that have recently experienced a flood event.

The VECM implementation for measuring the impact of monetary variables on Indonesia Property Prices Index

The basic human need for housing makes property investment safer than other sectors because demand tends to be stable despite changing economic conditions. The decision to invest is influenced by various aspects, including economic variables, including GDP, property credit growth, non-performing loans (NPLs), interest rates, exchange rates, and money supply, which are then examined for their influence on the Residential Property Price Index (RPPI) in Indonesia through this research using a Vector Error Correction Model (VECM) with quarterly data from 2003 to 2022. The findings show that GDP, interest rates, and money supply have a significant long-term impact on the RPPI. In the short-term, GDP and property credit growth have a negative impact on RPPI, while NPL and exchange rate do not. Causality tests indicate a bidirectional relationship between NPL and GDP with RPPI, with probability values exceeding 0.05. This study provides valuable insights into the monetary factors affecting residential property prices and suggestions for future research. AcknowledgmentThe authors gratefully acknowledge Ahmad Dahlan University for the support and facilities provided in conducting this research. They also greatly appreciate the academic resources that supported the data collection process and careful analysis of our findings. Besides, the authors express their gratitude to Bank Indonesia and Badan Pusat Statistik for their invaluable contribution to the data used in this research.

Effect of Price Earning Ratio, Earning Per Share, Projected Earning Growth, and Dividend Policy on stock prices of property and real estate sector companies listed on the Indonesian Stock Exchange in 2018-2022

This study's overarching goal is to quantify the impact on stock price of several financial variables such as price earnings ratio, earnings per share, projected earning growth, and dividend policy. Companies in the real estate and property industry that were listed on the Indonesia Stock Exchange between 2018 and 2022 make up the population of this study. To get a representative sample of seven businesses, use the purposive sampling technique. This is a study of deep data analysis approaches that use SPSS 26's multiple linear regression analysis. The research found that the price earnings ratio has a positive and statistically significant effect on stock prices, earnings per share has a positive and statistically significant effect on stock prices as well, projected earnings growth has a negative and statistically significant effect on stock prices, dividend policies has a negative and statistically insignificant effect, and all four of these factors have a positive and statistically significant effect on stock prices when taken together.

On the uncertainty of real estate price predictions

ABSTRACT Uncertainty quantification associated with real estate appraisal has largely been overlooked in the literature. In this paper, we address this gap by analysing the uncertainty in automated property valuations using conformal prediction, a distribution-free procedure for constructing prediction intervals with valid coverage in finite samples. Through an empirical study of property prices in the San Francisco Bay Area, we find that prediction intervals obtained using conformal quantile regression have exact coverage. In contrast, prediction intervals obtained from non-conformal quantile regressions severely undercover the data. Furthermore, we show that the intervals adapt to various characteristics of the dwellings, which is crucial given the heterogeneous nature of real estate data. Indeed, we observe that larger and older properties, those in both low and high-income neighbourhoods, as well as those on the market for less than one year are more challenging to evaluate.

Marriage entry, divorce and reconciliation: The unintended consequence of the home purchase restriction policy in China

This study investigates the unintended socioeconomic consequences of housing market regulations, focusing on the home purchase restriction (HPR) policy implemented in cities across China. While the primary aim of the HPR policy is to curb property prices by limiting the number of properties a household can purchase, its broader impact on family formation, particularly marriage and divorce patterns, remains underexplored. Applying Becker’s economic theory of marriage, which views marital decisions as rational choices influenced by socioeconomic factors, this study examines how the HPR policy affects family formation and dissolution across different city tiers and age groups in China. Our analysis reveals several key findings. First, the HPR policy effectively contributed to a decrease in house prices, aligning with its primary objective. However, it also led to delayed marriages among younger age groups, especially in first-tier cities, where housing affordability is a significant concern. Second, the policy inadvertently encouraged strategic divorces, allowing couples to circumvent purchase restrictions and acquire additional properties. This loophole undermines the policy’s effectiveness in reducing housing demand and stabilizing prices. These results highlight the complex interplay between housing market regulations and family demographics, emphasizing the need for policymakers to consider the broader societal impacts of non-family-oriented policies. As China, like many other societies, faces challenges related to the second demographic transition—such as declining marriage and fertility rates—the findings of this study serve as a cautionary tale. Housing policies, while effective in addressing market concerns, can have significant and sometimes counterproductive effects on family stability and formation, potentially exacerbating the demographic trends a society aims to mitigate.

Accounting issues in Calculating Taxes on Corporate Entities revenue of Entrepreneurs in Luang Prabang Municipality, Luang Prabang Province

In this study, the research team studied the accounting issues in calculating tax on corporate entities’ revenue of entrepreneurs in Luang Prabang Municipality, Luang Prabang Province. The purposes are to study the level of accounting problems in calculating the corporate income tax of entrepreneurs. To compare the level of accounting problems in calculating the corporate income tax of entrepreneurs by individual factors, the population used in the research was the accounting staff or entrepreneurs who calculated the corporate income tax of the business entities registered with the enterprises, a simple number of randoms 254 totally. The statistics used in the data analysis included descriptive and parameter reference statistics with the following values: frequency, percentage, mean, standard deviation, one-way ANOVA test, and paired test by LSD method. The results of the research were as follows: 1) The level of accounting problems in calculating the corporate income tax of business entities is as follows: Overall, it is at a moderate level which analyzed each aspect is sorted from large to small: the calculation of the depreciation of property prices in several levels, the improvement of accounting expenses tax is at a moderate level and the improvement of accounting income to a tax at a moderate level. ( 2 ) Comparison of the level of accounting problems in calculating the corporate income tax of entrepreneurs by classification according to personal factors such as classified by level of education found entrepreneurs there are different levels of education in accounting problems in calculating the corporate income tax varies two aspects are to improve the accounting expenses to be tax and to improve the accounting income to be tax and to classify according to the work experience of the entrepreneur found entrepreneurs with different work experience there are accounting problems in calculating the corporate income tax varies two aspects are the adjustment of accounting expenses to tax and the calculation of the depreciation of property prices.

Multiple Machine Learning Models in House Price Prediction: Performance Evaluation and Comparison

House pricing is one of the most critical concerns for people. Accurately predicting house prices is essential for participants and investors in the real estate market. This study delves into the realm of predictive modeling for house prices using a multimodal approach in machine learning. The research methodology spans various stages including data collection, preprocessing, feature engineering, and model selection. Based on a dataset sourced from Kaggle, housing data from Iowa comprising 79 pertinent features influencing property prices is utilized. Performance evaluation is conducted using the root mean square error (RMSE), comparing the efficacy of linear regression models such as Lasso regression, Kernel Ridge regression, and ElasticNet regression against tree-based models including XGBoost, Gradient Boosting, and LightGBM. Additionally, the study implements two stacked ensemble models to identify the most optimal predictive model for house prices. Experimental procedures involve training and evaluating these models on specific datasets to gauge their predictive accuracy. Findings and discussions underscore the comparative analysis of diverse machine learning models within the context of house price prediction. Moreover, the study employs the Gradient Boosting method in machine learning to pinpoint the ten most influential features impacting house prices. Recommendations for future research are provided to enhance the precision and robustness of house price prediction models. This research contributes to the advancement of predictive modeling in the real estate domain, offering insights into the efficacy of various machine learning techniques for forecasting property prices.

Determinants of Virtual Property Prices in Decentraland an Empirical Analysis of Market Dynamics and Cryptocurrency Influence

Determinants of Virtual Property Prices in Decentraland an Empirical Analysis of Market Dynamics and Cryptocurrency Influence

Understanding the spatially heterogeneous relationships between urban green amenities and residential dynamics: Evidence from Wuhan, China

Urban green turn has gained global momentum, with cities worldwide actively developing urban green amenities (UGAs). While considerable attention has been given to how expanding UGAs impact residential dynamics, the role of space in shaping these relationships has been underexplored. Employing global and local regression models, we investigate the associations between UGA characteristics, property prices and sales transactions, using data from 443 neighborhoods in Wuhan, China. Our findings reveal that at the city-wide level, larger UGAs are associated with gentrification trends marked by rising property prices and population shifts. High-quality UGAs are linked to higher risks of gentrification but do not significantly indicate direct displacement. Meanwhile, substantial bodies of water are associated with mitigating the effects of increased property prices and population shifts. At the local scale, these relationships show spatial heterogeneity, particularly between old inner-city areas and traditional industrial zones. Additionally, we analyze the roles of the state and real estate developers in influencing UGA development and the associated social ramifications in China. This study enhances our understanding of UGAs and their impacts on residential dynamics in a non-Western context, enriching the discourse on the social implications of urban green movements and ecological initiatives.

Business development strategy for the property sector after the COVID 19 pandemic in region-owned company subsidiaries of DKI Jakarta Province

The Covid-19 pandemic has led to a decline in property prices, with the property price index dropping from 209.8 points in 2019 to 187.43 points in 2020. The property sector in Indonesia is one of the sectors affected by a significant decline. Therefore, companies need to formulate business development strategies from the three business lines to improve their performance. The purpose of the study is to analyze internal and external factors that affect the business strategy of PT XYZ and to prioritize the most effective business development strategy for the company to adopt in response to changes in the property sector. The results of this research are expected to be applied as an alternative strategy in conducting business development in the future.

Influence of the Surrounding Environment Steam Power Plant (PLTU) Kema Satu Village, Kema District, North Minahasa District

In today's era, it is important to understand the impact of Steam Power Plant (PLTU) operations on the environment and economy around the plant site, especially in Kema Satu Village. PLTUs, which harness the kinetic energy of steam from geothermal heat, are widely used due to their ease of operation and accessibility of fuel. However, they also have significant impacts, both positive and negative, on local communities. This study aims to assess the environmental and economic impacts of PLTU Sulut 3 on the surrounding area, evaluate its operational efficiency, and determine its contribution to stabilizing and improving electricity supply in North Sulawesi and Gorontalo. The research method used was a combination of qualitative and quantitative techniques to ensure a comprehensive analysis. Data was collected through observation, interviews, and analysis of relevant secondary data. The results show that the PLTU in Kema Satu Village has significant impacts on the local environment, including air and water pollution, soil degradation, and ecosystem disruption. In addition to providing electricity supply and employment opportunities for the local community, the PLTU has also triggered property price inflation and shifts in the local economy. Other negative impacts include damage to the local agricultural sector and increased greenhouse gas emissions. The implications of this study emphasize the need for careful planning, community involvement, and the implementation of sustainable practices to mitigate negative effects on residents and the environment.