Future House Research Articles

Unpacking the relation between media sentiment and house prices: A topic modeling approach

This study uses a topic modeling approach to investigate the relation between news media sentiment and house price movements. By examining real estate related articles published in local newspapers of 16 major cities in Canada and Australia, we find that housing media sentiment has significant relation with future house price movements in the Canadian market but not in the Australian market. We identify the specific topics discussed in news media related to the housing market and report differences in their themes and media sentiment's predictive power between Canada and Australia. This analysis presents novel inferences of qualitative and hard-to-quantify information related to the housing market in two different countries.

빈집정비사업에 대한 공법적 검토

The recent issues of population decline due to aging and low birth rates have led to an increase in unoccupied houses. This increase is associated with higher crime rates, deteriorating residential environments, urban slums, imbalances in the real estate market, and environmental problems. The necessity of unoccupied house improvement has been raised, yet public law reviews on this matter remain insufficient. The concept of unoccupied houses includes both usable and unusable houses, which vary according to the investigation period and methods. Demolition targets include unusable unoccupied houses classified as Grade 3 under the ｢ACT ON SPECIAL CASES CONCERNING UNOCCUPIED HOUSE OR SMALL-SCALE HOUSING IMPROVEMENT｣ or as ‘specific unoccupied houses’ under the ｢AGRICULTURAL AND FISHING VILLAGES IMPROVEMENT ACT｣. The constitutional basis for the demolition of unoccupied houses includes the state's coordination and intervention duties under the principle of the social state, Article 122 of the Constitution, and the housing rights of neighbor of unoccupied houses. Especially, the conflict between the housing rights of neighbor and the property rights of unoccupied house owners(conflicts between fundamental rights) is transformed into a constitutional review issue as the legislator introduced a demolition system that limits the property rights of unoccupied house owners. The most important perspective in the constitutional review is adherence to the principle of proportionality, and demolition and compensation must be carried out under stricter standards, shifting from ensuring the existence of property rights to guaranteeing their value. The management and improvement obligations of unoccupied house owners are specified in the ｢AGRICULTURAL AND FISHING VILLAGES IMPROVEMENT ACT｣ but not in the ｢ACT ON SPECIAL CASES CONCERNING UNOCCUPIED HOUSE OR SMALL-SCALE HOUSING IMPROVEMENT｣. Based on the constitutional duty of property rights for public welfare and social constraints, owners may bear police responsibilities under public law, but this needs to be explicitly stated in the law. Such regulations on the responsibilities of unoccupied house owners can serve as the legal basis for enforcement fines and potentially a future unoccupied house tax when demolition orders are issued. Local governments' ex officio demolition of unoccupied houses is deemed an administrative execution. However, demolition orders for unoccupied houses are only feasible in cases involving substantial illegality, such as when legal buildings are abandoned for extended periods, resulting in significant harm to residents. In this context, recognizing the process of becoming substantially illegal should be strictly judged under the principle of proportionality. Compensation for the demolition of unoccupied houses is challenging to include in traditional public law compensation, thus falling under special public loss compensation recognized by the legislature through special laws. The compensation amount for demolition is determined as the arithmetic average of the amounts appraised by two appraisers. Issues concerning offsetting demolition costs highlight that compensation amounts can vary between voluntary demolition and enforced demolition by administrative execution, necessitating objective standards for demolition costs offset by administrative authorities.

Comparative Analysis of Multiple – Linear Regression Algorithm with Random Forest Regression for Prediction of House Plot Prices

Humans basically have a basic need to have a place to live, which can be a house or shelter. Along with the rapid population growth in Indonesia, which continues to increase every year, many people do not have or have a decent place to live. Therefore, careful planning is needed so that every family can have a decent home. One very important aspect in planning investment in the form of property is predicting future house prices. One approach that can be used is to use a Random Forest and Multiple Linear Regression algorithm, which is an algorithm from Machine Learning. There are several factors that can influence the price of a house, including land area, building area, number of bedrooms, bathrooms and garage. In this research, multiple linear regression and random forest regression methods were chosen. The aim of this research is to find the best prediction results between the two methods. To achieve accurate predictions, research was carried out repeatedly by dividing the dataset into 80% for training and 20% for testing. The research results show that the random forest regression algorithm provides the best results, with an accuracy of 81.6%.

Representing science: diversity on the President’s Council of Advisors on Science and Technology

Abstract Federal advisory committees related to science and technology are important mechanisms for connecting policy-makers with independent experts and the broader public. The balance of represented expertise and viewpoints shapes a committee’s intended advisory role, consensus building processes, and the quality and impact of its policy recommendations. This paper presents the first historical analysis of the President’s Council of Advisors on Science and Technology (PCAST) and examines its changing balance of social and professional perspectives from 1990 to 2023. We demonstrate that PCAST’s balance has shifted to be more inclusive of different social groups and professional backgrounds over time, particularly under President Biden. We conclude with recommendations for future White House science policy advisory bodies to ensure they are adequately representative of the diversity of perspectives in the US research enterprise and the overall US population.

Predicting UK Housing Price using Machine Learning Algorithms

The development of reliable predictive algorithm for house price as the housing market is a stand-out among the most involved regarding valuing the price and continues to fluctuate, is constantly a need for socio-economic advancement and welfare of citizen. In this paper, we develop machine learning algorithms for forecasting UK housing Price, and find an optimal algorithm that forecasts housing price accurately on the premises of the presence of many features or covariates. After applying correlation analysis to remove correlated variables in order to avoid multicollinearity, thereby increasing the statistical power, a novel method of using regression analysis to first of all understand and select statistically significant features for the various regions in England based on North South divide is adopted. These features are then used in the machine learning algorithm to further increase the statistical power of the algorithm, increase the level of accuracy for each of them and ultimately increase the predictive values for the algorithms.The model construction involves 3 stages: 1- correlation analysis to identify and remove correlated variables thereby avoiding multicollinearity and increasing the statistical power of the linear regression, 2 - using linear regression to determine variables that are statistically significant and 3 - building the machine learning algorithms based on the variables that are statistically significant from the linear regression. A comprehensive dataset of UK Paid housing Price from 2010 to 2019 was linked to a number of other datasets to generate a total 21 variables or features used for the models. Catboost, Gradient Boosting, Bagging, Random Forest, Extra Tree all achieved the excellent model’s performance result in all the regions considered. The comparison of the seven models showed that Extra Tree algorithm consistently achieved the best performance in term of level of accuracy in all the regions. K-Nearest Neighbours (KNN) is the only algorithm with less than 50% level of accuracy. Noticeably, the regions considered had varying or differing insignificant variables, implying that although many variables are common (statistically significant) to all the regions, there are regional differences and impact when modelling or predicting housing prices. This study validates the practicability of developing a machine learning methodology for the prediction of housing price. This research offers a reference for future house price prediction based on machine learning.© 2024 The Author(s). Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

A Futuristic Living Room Experience with Robotic Serenity

Automation, environmental consciousness, and robots are dancing on our houses more and more. Imagine your living room filled with friendly robots, air-purifying trees that swing to the beat of the sun, and vacuum cleaners that scutter over the floor. The intelligent design of household robots, environmentally conscious marvels like smart trees, and even the robotic makeover of the common vacuum cleaner are shaping this future—it's no longer science fiction. Developing technology that works with us, not against us, is the key. Adopting this human-centered strategy will enable us to build dwellings that are full of life, where technology speaks softly rather than loudly, and where convenience and sustainability go hand in hand. The future house is a symphony just waiting to be created, so let's welcome the robots, make friends with the cleansing trees, and enjoy the dance of the vacuums.

Telecommuting and Travel Behaviour: A Survey of White-Collar Employees in Adelaide, Australia

COVID-19 prompted a significant number of employees to shift to remote work for the first time, potentially reshaping future work patterns. This study examines the post-COVID impacts on telecommuting, commuting behaviour, travel activities, and lifestyles in the city of Adelaide, South Australia. A multivariate nominal logistic regression analysis of 305 post-restriction survey responses revealed that home distance from the CBD, industry, occupation, and income significantly influence post-COVID telecommuting trends. Individuals living over 20 kilometres from the CBD, those in professional or managerial roles, and higher-income earners (>125k) are more prone to regular telecommuting, highlighting the impact of commute lengths, job flexibility, and financial resources on the ability to work remotely. The study revealed a higher adoption of telecommuting post-COVID, with more individuals working from home and telecommuting more often each week. This led to reduced usage of private cars and public transport, indicating a decrease in overall travel frequency. Respondents also adopted flexible work schedules, resulting in fewer peak-hour commutes, which would have resulted in lower congestion and emissions and led to more sustainable travel practices. The study also investigated future telecommuting perspectives, revealing a preference for remote work 3–4 days a week. Some respondents who initially could not telecommute have since considered it feasible and want to adopt it. Notably, about 25% of respondents would even change jobs for flexible, home-based work arrangements. The study’s results suggest that remote work frequency may influence individuals’ future house location preferences. These findings offer valuable insights for sustainable transport and urban planning considerations in the post-COVID era.

A Comparative Study of Random Forest Regression for Predicting House Prices Using

Based on the rapid development of the real estate market, real estate prices in various regions of the world fluctuate greatly and are unstable, and we need to make some predictions for real estate prices. However, in reality, we pay too much attention to the relationship between past property prices and current property prices and often ignore the prediction of future house prices. Research on predictive models is lacking. Therefore, studying real estate forecasting models is one of the best solutions to solve the problems faced by the real estate market based on the thinking of the current situation. In response to this problem, I propose to use a random forest model, gradient boosting, and optional to build a reasonable predictive model. The final results prove that this predictive model can be used to some extent to predict changing real estate prices in the future market. It is hoped that the method in this paper can provide a reference for subsequent research on predictive models.

Cointegrations in house price dynamics and ageing population risks.

How does the riskiness of an ageing population change with house price dynamics of rural areas? Why do rural house prices increase faster than cities despite their ageing populations? Life cycle theory predicts working age households have higher demand for housing than retirement households. An issue that has seen much less attention in the literature is that rural house prices have been increasing despite their populations age rapidly. To answer these issues, our paper introduces an empirical cointegration-based framework designed to be flexible for empirical settings. Our cointegration framework reveals crucial information about rural housing and ageing which has not been found previously: the short-term deviation of house prices from cointegration restrictions is a strong predictor of future rural house prices and migration rate from 1 to 4 year ahead. This is not the case for urban areas nor where cointegration restrictions are being ignored. Rural house prices, not urban ones, are the key to understand this cointegration restriction. Our framework is pertinent to most ageing societies with available housing and demographic data. When a government formulates macroprudential policies internalizing these cointegration restrictions and supporting rural developments, migration into rural areas and population increases are possible. Our evidence highlights the importance of cointegration-based long-run ageing risks for rural housing markets.

Determinants of housing prices: evidence from East Coast Malaysia

Purpose The purpose of this study is designed to achieve the learning process in producing studies involving economic issues and scenarios in business management in Malaysia. In addition, this study will provide exposure to the integration of managerial skills by using both microeconomics and macroeconomics concepts and theories to aid decision-making in a business environment. Design/methodology/approach The research method comprised qualitative methodology of literature review, case study and quantitative methodology of multiple linear regression (MLR). In this case, seven microeconomics and macroeconomics factors which are believed to significantly affect house price index (HPI) are taken into consideration which includes gross domestic product, consumer price index (CPI), government tax and subsidy on housing, overnight policy rate, unemployment rate (UNEMP), the median income (INC) and cost of production index. Findings This research has resulted in three significant factors affecting HPI from MLR, which include CPI, UNEMP and INC where the increase of these factors will cause a high increment of HPI. The other four factors are not significant. Originality/value Malaysia has been facing the stagnancy in house market these recent years due to issues such as massive oversupply, impacting Malaysia’s economy specifically focusing on domestic direct investment. To avoid oversupply issues, the vitality of future house demand and pricing forecast should be comprehended by involved bodies for more effective planning for the house development industry. To make a better and bigger impact, this research is intended to analyse the microeconomic and macroeconomic factors affecting the HPI to better understand the significance of each of these factors to the changes of HPI to resolve these economic issues.

What is behind housing sentiment?

We utilize topic modeling to analyze the relation between housing media sentiment and house price changes in the United States. We uncover seven distinct topics related to housing market in articles published in the Wall Street Journal, New York Times, and USA Today during the 2000–2021 period. Not all topics show significant relation to house price movements. Sentiments of articles related to economic outlook and house construction are most significant in explaining future house price changes suggesting that news media discussions about economic fundamentals stand behind the link between news media sentiment and house price changes.

The evolution of female space in Korean vernacular houses in Yanbian: the influence of social logic on spatial configuration

PurposeThis study explores the characteristics of female space evolution in Korean vernacular houses in the Yanbian region of China. In addition, it discusses the influence of social logic on the evolution of female spaces.Design/methodology/approachThis study utilises space syntax methodology to examine the evolution of female spaces in Korean vernacular houses in terms of connectivity value, step depth and integration value. Furthermore, it conducts an analytical exploration of social logic based on the evolutionary characteristics of female space.FindingsThe findings elucidate the evolutionary characteristics of the spatial configuration of female spaces in Korean vernacular houses, with differential changes in connectivity, a gradual tendency towards openness and simplicity and increased accessibility and centrality. This reflects the changing spatial needs of Korean women brought about by changes in lifestyle, consciousness, social status and family structure.Research limitations/implicationsThis study provides perspectives and insights into the vernacular architecture and architectural sociology of ethnic minorities in regions of China and Asia. Furthermore, it can provide relevant construction organisations with a more intuitive understanding of Korean vernacular houses and a reference for future house renewal and construction in the Yanbian region.Originality/valueAlthough many studies have investigated various aspects of Korean vernacular houses and female spaces, none have examined the influence of social logical changes on the evolution of female spaces in Korean vernacular houses. Thus, this study is valuable and novel.

How Do Support Pressure and Urban Housing Purchase Affect the Homecoming Decisions of Rural Migrant Workers? Evidence from Rural China

Talent revitalization is the basis of rural revitalization, and the return of migrant workers to their hometowns is a critical way to improve rural human capital. Based on the perspective of individual–family interaction and collaboration, we constructed a theoretical model for maximizing the net benefits of rural migrant workers. Then, we use it to explore the impact of family support pressure and urban housing purchase on individuals’ homecoming decisions. Firstly, we find the odds ratio of migrant workers with support pressure to return home is 14.013 times higher than those without, and the odds ratio of migrant workers with urban housing is 42.94% lower than those without. Secondly, in the process of supporting, the family, as a link between individuals and rural society, can enhance the connection for migrant workers, thus promoting their return behavior. The mediating effect of hometown connection is 1.342, accounting for 50.83% of the total effect. However, buying a house in the city reduces individuals’ homecoming behaviors by encouraging “trailing spouse”. Thirdly, individuals’ homecoming intention is not consistent with their behavior. The moderating effect of a future house purchase plan changes the influence of support pressure on individuals’ intention to return home to some extent. Finally, we should further strengthen rural infrastructure construction and elderly care service supply to reasonably guide capable and willing talents to return to the township. This study provides some implications for the revitalization of rural talent.

The time-varying impact of foreign capital flows on house prices: evidence from South Africa

This study employs a time-varying parameter VAR with stochastic volatility (TVP-VAR) to investigate the time-varying impact of foreign capital flows on house prices in South Africa. The study also analyses the behaviour of the house price index using recursive unit root tests. The results of these tests show a statistically significant period of house price exuberance. The results of the TVP-VAR show a contemporaneous positive impact of foreign capital on house prices throughout the sample period, with the largest impact occurring during the period of house price exuberance. In addition, the international capital flows are found to aggravate the imbalances between demand and supply sides, thus enforcing the future house prices to comove with the housing supply.

Feeling comfortable with a mortgage: The impact of framing, financial literacy and advice

AbstractThe family home is the most important asset on household balance sheets, aside from human capital. Choosing a suitable mortgage is therefore critical to financial well-being but households often make costly mistakes. We collect data in an online survey to test borrowers’ comfort with, and understanding of, mortgage debt. We analyze the impact of financial literacy, mortgage broker advice and whether the loan is framed as a lump sum debt or an equivalent stream of repayments. We conjecture that participants’ comfort with loans and their ability to match lump sum debt to equivalent repayment streams will help them to choose a suitable mortgage. Results show that participants with high financial literacy are less comfortable with mortgage debt in general and also less sensitive to framing than those with low financial literacy. Literate participants are better able to match repayment streams with the equivalent lump sums. Endogeneity-controlled regression analysis shows that consulting brokers leads to higher comfort with debt and lower sensitivity to framing. Survey responses also indicate more uncertainty about future house prices among borrowers who intend to consult brokers than among those who do not.

Comparative Analysis of Machine Learning Performance in House Price Prediction for Ames Iowa

The price of housing is closely related to the quality of people's lives, which is reflected in various aspects (e.g., finding a place to live and managing their finances). On this basis, being able to predict house prices would be a great convenience, and this study examines the accuracy of multiple mechanistic learning regression models in predicting future house price changes. To be specific, this paper downloaded the dataset from Kaggle which includes the training and testing csv files. and ran many different regression techniques including lasso and more models on google Colab. Furthermore, a comparison between the results of different methods is carried out by estimating the mean square error. Then, this research combined some of the best yielded results of the model to combine into a final model and got a result with a high score. According to the analysis and evaluations, the study concluded that the hybrid model resulted in a more accurate prediction than the single model. These results shed light on guiding further exploration of real estate prediction.

Price Prediction of Ames Housing Through Advanced Regression Techniques

House price fluctuates at all times, and it is necessary to predict the future house prices for the reason that it is crucial for buyers, property investors and the economy as a whole. This article reveals the important features relating to house price and demonstrates the method of predicting house prices in Ames, Iowa through advanced regression techniques, such as correlation, feature engineering, and model building. The prediction is performed through machine learning methods based on the dataset containing almost all the features of residential houses in Ames, Iowa. The dataset was originally splitted into the ‘train set’ and the ‘test set’. Six models were chosen, LASSO Regression, Elastic Net Regression, Gradient Boosting Regression, XBoost, LightGBM, and Stacked Model, for evaluation under the five-fold cross-validation to check the performance of the algorithms and report the root-mean-square logarithmic error. Eventually, Gradient Boosting Regression Model is chosen by the lowest root-mean-square logarithmic error of 0.04615 and a low cross-validated value of 0.1174 to predict the house price to benefit buyers, investors, and the whole society.

RHPMF: A context-aware matrix factorization approach for understanding regional real estate market

The real estate market has a significant impact on people’s daily life. Therefore, it is crucial to understand the real estate market from both spatial and temporal perspectives, while there is still a lack of research in real estate industries. In this paper, a regional house price mining and forecasting (RHPMF) framework is proposed to help people intuitively understand the spatial distribution and temporal evolution of the urban estate market based on real-world housing data and urban contexts such as demographics and criminal records. Specifically, the RHPMF framework introduces a context-aware matrix factorization to extract crucial spatial and temporal price factors for revealing the housing market. Meanwhile, the RHPMF can forecast future regional house prices by manipulating the two price factors. Consequently, this study presents extensive exploratory analysis and experiments in Virginia Beach, Philadelphia, and Los Angeles to verify the proposed RHPMF. These case studies indicate that the RHPMF framework can accurately capture the market’s spatial distribution and temporal evolution and forecast future regional house prices compared with recent baselines. The experimental results suggest the great potential of the proposed RHPMF for applications in real estate industries.

Passive House performance evaluation and key management factor analysis in the Sino-German Ecopark: A study through fieldwork and interdisciplinary joint research

In terms of eco-friendliness, China is far behind advanced western countries. Recently, however, several places in China have been collaborating with the German Passive House Institute to construct eco-friendly buildings on a large scale. This study intends to analyze the passive house construction technology being implemented in the eco-friendly buildings that have been completed or are under construction at the Sino-German Ecopark in Qingdao, among several other complexes, based on fieldwork and interdisciplinary joint research. This study compares the importance of key factors through analytic hierarchy process (AHP) analysis. This study aims to extract and present factors of high difficulty noted in the successful construction of passive houses by examining the fieldwork of three case projects at the Sino-German Ecopark. These factors were divided into three categories: technical factors, policy factors, and managerial factors. Through AHP analysis, it was determined that the policy factors were of higher importance than technical and managerial factors. Barrier-free facilities were the most important factor among all the policy factors, and the window system was the most important among the technical factors. In the case of management factors, project management ability proved to be the most important factor. The findings of this study are expected to serve as a guideline that should be considered in the future Passive House design and construction process.

The evolution of functional organization in vernacular Gyalrong Tibetan houses: a quantitative investigation

ABSTRACT The Qinghai-Tibet Plateau has many traditional dwellings that are suitable for research into architectural design. However, the current state of research on vernacular Tibetan houses is still limited by study regions, sample sizes, and historical perspective. By selecting 82 folk houses in the Gyalrong Tibetan area as study objects, this work aims to (1) record a fading tradition of vernacular architecture assisted by computer modelling and mathematical statistics; (2) analyse the historical evolution of the functional organization of the houses; and (3) explore the reasons for this evolution to give suggestions to contemporary architectural designers. The investigation reveals that (1) the evolution of the functional organization between old and new houses primarily includes an increase in the number of bedrooms and toilets and a decrease in the area of storage spaces and corrals and (2) the historical reasons for this evolution include changing concerns about defence, sanitation, and the tourism industry. Finally, suggestions for future house design are given in terms of the arrangement of building volume, the arrangement of central communication spaces, and the separation of spaces for landlords and guests.