House Sale Prices Research Articles

Predicting housing sale prices in Germany by application of machine learning models and methods of data exploration

Predicting housing sale prices in Germany by application of machine learning models and methods of data exploration

Legality of Environmental Coordination Costs in The Construction of Housing Projects As A Form of Corporate Social Responsibility (Home Price Component Study)

This research aims to find out the application of Corporate Social Responsibility (CSR), to know the setting of environmental coordination costs charged to developers in housing construction against the sale price of houses in PT Harapan Inti Persada Indah (PT HIPI). This research is a type of normative jurisprudence, with legislative approaches, case approaches and conceptual approaches carried out around the area of serpong garder II and the office environment of PT HIPI. Using primary legal material sources such as the results of observations and interviews. The results of the research that PT HIPI has implemented its social responsibility but has not implemented it is not in accordance with the regulations of the legislation. As far as environmental coordination costs can make as CSR costs as well as influence the component of house sales price in PT HIPI.

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.

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.

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.

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.

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.

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.

Estimating proximity effects to wildfire fuels treatments on house prices in Cibola National Forest, New Mexico, USA

Forested landscapes in the Western United States are subject to growing size and severity of wildfires, in part due to historical management strategies focusing on wildfire suppression. Forest restoration treatments and fuels reductions, including thinning and prescribed burning, can reduce the frequency and intensity of wildfires. Extensive restoration and fuels treatment efforts are underway across many areas in the Southwestern United States, including New Mexico. The tradeoff between amenity values provided by forested landscapes and the wildfire risk associated with forested landscapes is becoming increasingly important to understand as development in the wildland-urban interface increases. Understanding how house proximity, relative to forest restoration or fuels treatments, is capitalized into home sale prices can provide useful information about how individuals value forested landscapes that have been altered to reduce wildfire risk or severity. We use a Hedonic Property Model to estimate the average treatment effect of proximity to forest restoration or fuel treatments in New Mexico, United States. We use matching methods to estimate the average treatment effect of proximity to forest restoration. We find that proximity to the forest has a positive amenity value; however, proximity to recent forest restoration or fuel treatments results in a decrease in house sale prices. We combine the results of our two models and calculate that homes not within one kilometer of a treated forest and within one kilometer of Cibola National Forest sell for an average $73,626 premium. The average premium drops to $22,996 for homes within one kilometer of a forest that has been recently treated and within one kilometer of Cibola National Forest.

Estimate House Price Using Machine Learning

The House Price Index (HPI) is commonly used to estimate the changes in housing prices. The sale price of the house is correlated with many other factors like geographical location, size of the house, age of the house, the area and population of the neighborhood etc. There has been a considerably large number of datasets released in the literature of various locations to explore the correlation of the sale price of houses with their corresponding features. However, all the features don’t affect the sale price in equal proportion. Some features strongly correlate with each other and, while some features don’t carry any importance or are probably redundant. As a result, to explore various impacts of features on sales prices, we performed a detailed data analysis on the original house dataset. This report also comprehensively validates multiple steps of data analysis with supporting statistics and visualizations to provide an optimistic result of various features and their impact on the sales price of houses.

Utilizing Multilevel Modeling to Measure Neighborhood Dynamics and Their Impact on House Prices

For decades, researchers have been concerned with house price modeling, and recognition has grown for the necessity of considering environmental and contextual variables in the process. This research examines the association between neighborhood characteristics and all individual house sale prices in Lucas County, Ohio, from 2012 to 2016, through a multilevel modeling (MLM) approach. Although there are various ways to define neighborhoods, census tracts and school districts are used in this study. Neighborhood characteristics include a foreclosure score, race heterogeneity, median household income, and built environment variables, such as walkability indexes. School district characteristics include student performance, tuition expenditures per pupil, and percent of expenditures spent on classroom instruction. The advantage of the multilevel model, is that it allows us to derive reliable estimates of place differences, representing a considerable improvement over the single-level model. Significant correlations were identified between house prices and foreclosure score, student performance, and tuition expenditures per pupil. The MLM results indicate that house prices not only lay in house characteristics themselves but also in neighborhood features, thus MLM offers good prediction accuracy and high explanatory power.

Decomposing the impact of immigration on house prices

How does an increase in immigrant inflows affect housing demand and prices for a given housing supply? In this paper, I show that we can formally decompose total demand changes into those from the immediate increase in population due to the new arrivals (the “partial effect”) and additional changes from relocated natives (the “induced effect”). I propose and apply a method to estimate these effects separately, exploiting data for Spain between 2001 and 2012. Using an instrumental variables strategy, I find that a one percentage point increase in the immigration rate raises average house sale prices by 3.3%. Partial demand estimates are 24% lower than total estimates due to immigrants and natives locating in the same provinces. The results show that accounting for the impact of immigration on native mobility is central to understanding net demand adjustments, as partial and total effects can significantly differ depending on native population relocation.

AI 스마트 하우징 구축이 서울시 아파트 거래가격에 미치는 영향

The AI smart housing market is expected to grow, but there is no study on the effect of the installation of AI smart housing on housing prices in terms of activation. It was attempted to demonstrate whether there is a housing price as a characteristic of a house through the hedonic model, which is most commonly used.
 The empirical data was used by combining the K-apt apartment management fee disclosure data and the apartment price and apartment characteristics data provided by Real Estate 114 Reps. According to the apartment management cost information provided by K-apt, 2,144 (12.15%) of 17,638 apartment complexes nationwide for which the AI Smart Housing maintenance fee is charged as an AI Smart Housing were installed. In the case of Seoul, an AI Smart Housing was installed in 213 (8.59%) of the total 2,479 apartment complexes.
 As a result of attaching price information to the management cost information, a total of 12,073 apartment transaction price data were used, and 1,598 pieces of AI Smart Housing installation complexes were used for the analysis. As a result of the empirical analysis, as in the general hedonic model, the house sale price per unit area was higher as the transaction level increased, the number of years elapsed, the total number of households, and the common management cost per household increased. In the case of common management fees, in the case of apartments that are recently supplied, the common management fees are high according to the trend of supplying various resident convenience facilities such as swimming pools and golf driving ranges in the complex, as well as large lobby spaces, and this can be seen as a result of this part being reflected in the house sale price. . In addition, the fact that the smaller the exclusive area is, the higher the price per unit area is due to the fact that the preference for small flats due to the downsizing of the household size, such as the increase in one- or two-person households, is reflected. Lastly, it was found that the apartment price per unit area was higher when the AI Smart Housing, the subject of this study's interest, was installed, compared to the case where the AI Smart Housing was not installed.

Determining the Construction Costs for Basic Type to Estimate the Sale Prices of New Multi-Family Housing Projects

Over the past two decades, the South Korean government has been regulating the supply and prices of multi-family housing (MFH) projects to stabilize the national population. Recently, active research has been conducted on the construction costs for basic type (CCBT) calculation to formulate appropriate policies. However, related previous studies have focused on improving the predictability of the construction cost in early stages based on existing house sale prices. In contrast, the CCBT calculation approach mainly requires policy implementation in practical fields, without considering the requirements of academics. Therefore, it is necessary to academically discuss a different approach for the estimation of sale prices of new MFH in the construction stage. This study aimed to calculate the CCBT to determine the appropriate sale price for new MFH. We selected four sample projects to calculate the CCBT, and a weighted average method was applied to correct regional deviations. Case application, which is a comparison between the CCBT-based sale price and actual case-based sale price, produced cost values in the range of 98–104%, and they included additional expenses. The results of this study demonstrate an extremely high level of cost estimation accuracy according to the Association for the Advancement of Cost Engineering study. Furthermore, this study can facilitate the stabilization of national housing by determining an appropriate sale price and can contribute to cost management research conducted during the construction phase.

지역별 주택의 시장근본가격(market fundamental price)추정에 관한 실증연구

In this study, the market fundamental price of house price was estimated from August 2001 to April 2022 in based on the present value model and compared with the actual house sale price.
 As a result, First, the pattern of the difference between the actual housing sale price and the market fundamental price of each model was similar in other regions except Incheon, and although it was not exactly the same, it was divided into four periods. Second, The market fundamental price of houses estimated according to the three models showed different volatility by period and region. Third, the estimate of the third model showed less volatility compared to the estimates of other models and closely followed the actual apartment sale price. Fourth, Factors the interest rate and rent growth rate which are factors constituting the market fundamental price had different effects depending on the period.

Understanding the Accessibility of Urban Parks and Connectivity of Green Spaces in Single-Person Household Distribution: Case Study of Incheon, South Korea

Given the rapidly increasing need for policies with regard to single-person households in Korea, this study examines the effects of park accessibility and the connectivity of green spaces on the spatial distribution of single-person households. SK-Tmap API and Conefor 2.6 are used to analyze park accessibility and green space connectivity, respectively. Multiple and spatial regression analyses are conducted using variables for the following three characteristics: park and green space, housing, and region. The findings show that generalized Betweenness Centrality–Integral Index of Connectivity based index (dBC_IIC), apartments, studio apartments, housings larger than 85 m2, distance to welfare facilities, and population density had a positive association with the spatial distribution of single-person households, while park accessibility, difference in Number of Links (dNL), generalized Betweenness Centrality–Probability of Connectivity based index (dBC_PC), and housing sale prices had a negative relationship. Regression analyses are further conducted for different age groups (10–20 years, 30–50 years, and over 60 years). In terms of park connectivity, dBC_PC showed a negative effect and dBC_IIC had a positive effect for the 10–20 age groups, while the 30–50 age group showed the same result as that of all single-person households. For single-person households over 60 years of age, no connectivity index was found to be significant. Policy implications are made in the short- and mid- to long-term for strengthening the connectivity of parks and green spaces in the study area. The results of this study can be used as an important guideline for establishing park and green space plans in consideration of single-person households in the future.

House Price Prediction based on ML using Regression Techniques

Guessing models for determining the sale price of houses in cities like Bengaluru still serve as a challenging and deceptive task. The retail price of buildings in cities such as Bengaluru depends on the number of other items. Important factors that may affect the price include the location, location and location of its facilities. In this research project, analytical research was conducted by considering a data set that is always open to the public by displaying available housing structures in the form of a machine hackathon platform. The data set has nine features. In this study, an effort has been made to develop a predictive model for price analysis based on price factors. Modeling tests use some retraction techniques such as multi-line retrieval (Small Squares), Lasso and Ridge retrieval models, vector retrieval, and reinforcing algorithms such as Extreme Gradient Boost Regression (XG Boost). Such models are used to create a predictive model and to select the most efficient model by performing comparative analysis and predictor errors found between these models. Here, the effort is to build a pricing model for price analysis based on price factors.

Does energy efficiency matter for prices of tenant-owned apartments?

In this study, we use Energy Performance Certificate (EPC) information to investigate the effect of energy efficiency on the selling price of Swedish tenant-owned apartments. While there is a large body of literature on how energy efficiency affects the sales price of single-family houses, none has exclusively focused on tenant-owned apartments. For owners of tenant-owned apartments in Sweden, heating is for a large share included in the monthly fee paid to the tenant association, which usually does not change on a short-term basis. This raises the question whether homebuyers’ incentives for acquiring energy-efficient tenant-owned apartments are large enough to be capitalized into the prices. By hedonic models and matching methods, we found mixed results. In our most optimistic scenarios, tenant-owned apartments enclosed in energy-efficient buildings are sold with a premium of approximately 0.8 to 1.2% compared to apartments in non-efficient buildings. The results in this study are not robust to all model specifications and vary across regions. In comparison with recent studies using data for single-family houses in Sweden, our detected capitalization is smaller. Our results highlight a need for targeted measures if EPCs are to be fully capitalized in prices for all dwelling types in which buyers have different economic incentives for reducing their energy consumption.

Incorporating spatial autocorrelation into house sale price prediction using random forest model

AbstractThe random forest model has been frequently used for house sale price prediction because researchers have shown that it can create better results with smaller prediction errors than conventional statistical models. However, house sale prices tend to be spatially autocorrelated due to shared neighborhood environments and similar physical characteristics, which have been rarely considered and accounted for in the random forest model. A failure to address spatial autocorrelation may result in over‐ or underpredicted house sale prices, especially when these sale prices are highly spatially dependent. This research proposes an extended random forest approach, introducing additional proxy variables that furnish geographic proximity measures of house locations in order to capture spatial autocorrelation that is unexplained by covariates. An application of the extended random forest approach for house sale prices in Fairfax County, Virginia, confirms the effectiveness of the proxy variables with an improvement of house sale price prediction accuracy. The introduction of the proxy variables can correct over‐ and under‐predicted house sale prices that show an underlying spatial structure.

Impact of urban railway announcements on housing market: Preponderance of pre-construction and post-construction phase effect of urban railway projects

The increasing concerns arising from measuring the effect of metro railway line projects on the sale prices of surrounding housing within urban spaces have been addressed in a vast number of related studies. However, potential changes in these effects with regard to short-run and long-run equilibriums have not been studied satisfactorily in developing countries. Therefore, this paper measures the effect of metro railway line investments on housing sale prices with respect to two different stages of related projects: pre-construction stage and post-construction stage, in two similar study areas in İstanbul, Turkey. The prices of houses are modeled using hedonic price models. The results indicate that housing sale prices before construction (announcement) of the metro railway line projects increase almost twofold after construction. In addition, the paper employs a before and after analysis to measure the effect of the different stages of metro railway line investments on housing sale prices. The results reveal that the average price increase of a housing unit in the pre-construction phase of the metro railway line project is 86,756.37 TL greater than the average price increase in the post-construction phase. Thus, the validity of the speculated effect of such urban railway investments on housing sale prices is justified.