Number Of Bathrooms Research Articles

Research on the Influencing Factors of House Prices in Chicago

This study provides a comprehensive analysis of the factors that influence housing prices in suburban Chicago. Using detailed regression analysis, several key variables were identified as significant predictors of property value. The study included variables such as the number of bedrooms, square footage, number of rooms, size of the lot, annual taxes, number of bathrooms, number of garages, and the condition of the house. The R-squared value of the model is 0.732, indicating that approximately 73.2% of the house price variance can be explained by the selected variable. Houses with more rooms and more square footage tend to cost more. In addition, characteristics such as the number of garages and bathrooms can also have a significant impact on house prices. These findings provide valuable insights for real estate professionals, buyers and sellers, enabling them to make informed decisions regarding real estate investment and pricing strategies. By understanding the key drivers of house prices, stakeholders can better navigate the suburban housing market and ensure more accurate assessments and strategic planning.

Uncovering urban water consumption patterns through time series clustering and entropy analysis

Sustainable urban water management is crucial for meeting the growing demands of urban populations. This study presents a novel approach that combines time series clustering, seasonal analysis, and entropy analysis to uncover residential water consumption patterns and their drivers. Using a three-year dataset from the SmartH2o project, encompassing 374 households, we identify nine distinct water consumption patterns through time series clustering, leveraging Dynamic Time Warping (DTW) as the optimal similarity measure. Multiple linear regression reveals key household characteristics influencing water usage behaviors, such as the number of bathrooms and appliance efficiency ratings. Seasonal analysis uncovers temporal dynamics, highlighting shifts towards lower consumption during summer months and increased variability in transitional seasons. Entropy analysis quantifies the diversity and complexity of water consumption at both cluster and household levels, informing targeted interventions. This comprehensive, granular approach enables the development of personalized water conservation strategies and policies, empowering water utilities to optimize resource management and contribute to sustainable urban water practices.

Impact of Green Features on Rental Value of Residential Properties: Evidence from South Africa

In recent years, scholars have called for an increase in the usage of green features in the built environment to address climate change issues. Governments across the developed world are implementing legislation to support this increased uptake. However, little is known about how the inclusion of green features influences the rental value of residential properties located in developing countries. Data on 389 residential properties were extracted and collected from a webpage. Text mining and machine learning models were used to evaluate the impact of green features on the rental value of residential properties. The results indicated that floor area, number of bathrooms, and availability of furniture are the top three attributes affecting the rental value of residential properties. The random forest model generated better predictions when compared with other modelling techniques. It was also observed that green features are not the most common words mentioned in rental adverts for residential properties. The results suggest that green features add limited value to residential properties in South Africa. This finding suggests that there is a need for stakeholders to create and implement policies targeted at incentivising the inclusion of green features in existing and new residential properties in South Africa.

Rancang Bangun Sistem Prediksi Nilai Jual Properti Berbasis Web Menggunakan Regresi Linear

This research was conducted to provide a clearer and more accurate depiction of the property value at Ziva Graha Property based on the physical characteristics and location of the houses. The study utilized PHP for data processing and modeling. The data employed included information on land area, building area, year of construction, number of floors, number of bedrooms, number of bathrooms, number of garages, and selling prices. This data was explored and processed using PHP software to assess the efficacy of linear regression methods in predicting house values. The research findings indicate that variables such as land area, building area, and number of bedrooms significantly influence the house's selling price at Ziva Graha Property. Other variables, such as the number of floors, number of bathrooms, distance to the city center, and road width, have a smaller impact. It is expected that the results of this research can provide a useful guide for prospective buyers and sellers in determining fair and realistic prices based on the physical characteristics of the houses.

FACTORS AFFECTING HOUSING PRICES: THE CASE OF ISTANBUL-ATAKOY

Different structural and spatial features such as type of housing unit, age of housing unit, size of housing unit, number of rooms, number of bathrooms, construction quality, heating system, parking lot, quality of public services, proximity to centers, distance to health and education facilities, transportation facilities, crime rate, satisfaction with social activities, landscape, environment, pollution and airport noise affect the prices of the housing units. Atakoy is one of the important residential areas of Istanbul with a high quality of life, due to the richness of its green areas and its urban texture, the closeness to centers, high quality of transportation and urban services. The lack of vacant land, the high demand and the high occupancy rate in the region have increased the housing prices considerably, and the importance of the regional housing market. The aim of the study is to determine the characteristics of housing unit that may affect the housing prices in Atakoy. In this context, multiple regression analysis has been made with the data which consist of physical information about 149 housing units for sale in the region and it has been examined which features related to the housing unit how much may affect the housing prices.

Prediction of House Prices in Lagos-Nigeria Using Machine Learning Models

This paper considers the relationship between the price of houses and the features namely the number of bedrooms, parking space, and different house types. In this study, a machine learning approach was used to develop prediction models that predicted house prices in Lagos. Different machine learning techniques were used, train-test split to split the data into training sets for training and building the model and test data to test the accuracy of the model, performance metric mean absolute error to set the baseline for the model, Variance Inflation Factor (VIF) to help remove multicollinearity between features and Streamlit interactive dashboards to communicate with the model. Correlation and regression methods were used to examine the relationship and build the model. It is observed that there is a strong positive correlation between the number of bedrooms and the number of toilets, likewise the number of bedrooms and the number of bathrooms. It also shows that there is a moderate positive correlation between the number of bedrooms and price. The model shows that the number of bedrooms, parking spaces, and house types play an important role in determining the price of houses.

Sales rate forecasting of single-detached houses using artificial neural network technique

Although the predicted number of sold units per month is one of the most necessary information to study the feasibility of detached-housing estate projects, traditional forecasting methods are limited. The research objective was to apply the Artificial Neural Network (ANN) technique to develop a sales rate forecasting model by compiling factors from an appropriate literature review. Then, 100 housing project data were collected from market research reports and the projects' websites and analyzed using the ANN technique. The results showed the ANN network with 16 input nodes from 10 factors: Selling price, Number of bathrooms, Number of bedrooms, Distance from the main road, Distance from the bus stop, Distance from the expressway, Distance from the metro station, Distance from the gas station, Distance from the shopping mall, and Project location zone. The acquired model had a Root Mean Square Error (RMSE) of ±6.296, and the slope and R2 values of the linear regression analysis between the forecasted rate and the actual rate were 0.620 and 0.571, respectively. The findings guide real estate developers and academia on the factors affecting the sales rate and provide a decision support model for investment and design of projects and confirm the potential of ANN in solving the problem with limited numbers of data.

Analisis Perbandingan Metode Regresi Linier, Random Forest Regression dan Gradient Boosted Trees Regression Method untuk Prediksi Harga Rumah

The need for a place to live is one that many people prepare, both millennials and adults and the elderly. With the continued increase in population growth in Indonesia and increasing public interest in buying a place to live early on, this can make not all groups of people have a place to live or a house that is quite livable. Related to this, the public needs up-to-date information related to predictions of house prices both for housing and second-hand housing prices for planning purposes in the future. The purpose of this study is to carry out a comparative analysis of the prediction results of house prices with several Machine Learning algorithms consist of Linear Regression, Random Forest Regression and Gradient Boosted Trees Regression. Evaluation for all the method applying Cross-Validation. The evaluation is seen from the smallest Root Mean Square Error (RMSE) error rate of each testing method. The results of this study are the Random Forest Regression obtained an RMSE value of 0.440, the Linear Regression model obtained an RMSE value of 0.515 and the RMSE value of Gradient Boosted Trees Regression of 0.508. The results were obtained from testing a dataset of 2011 records with a division of 80% for data training and 20% for data testing, the data has 6 attributes used in testing including house prices, land area, building area, number of bathrooms, number of bedrooms and the number of garages. In this study, prediction results using the Random Forest Regression method yielded the highest accuracy of 81.5% compared to the Linear Regression and Gradient Boosted Trees Regression methods.

Using LADM and BIM for property and tourist taxation in the municipality of Bar, Montenegro

Property valuation and taxation processes are of great importance, since revenues from taxes can be allocated to other areas of public interest thus improving the quality of life for all citizens. Two types of taxes are related to buildings and building units in Montenegro: property tax that is paid for all properties and tourist tax that is paid for properties used for tourism. Montenegro is a coastal country with rapid urbanization and developed tourism, so it is important to have well organized cadastral and tax administration records in order to perform correct tax calculation. Since the newly proposed LADM Valuation Information model is planned to be the part of the second edition of ISO 19152 Land Administration Domain Model (LADM), it is considered in the development of the country profile for valuation and taxation for Montenegro. The resulted model is presented in the paper. In addition to data organization, the procedures for determining the value of the property and tax value are also important and should be implemented according to the national regulation. In the paper, the formulas for calculation the tax values are presented. Appropriate attributes and classes that ensure keeping necessary data are added to the model. This model shows that LADM Valuation Information model can be used also for a very specific framework of legislation in which special kinds of property taxes can be found. The tax calculation in practice is not always correct due to lack of data. The possibility of using BIM model for new buildings in tax calculation, in order to overcome the mentioned problem, is analyzed. Data such as the area of the building and the quality of the building (building construction, building facade, windows, number of rooms, installations, number of bathrooms, etc.) can be directly loaded into tax administration database from the appropriate BIM model thus making the tax collection system more equitable and correct. In the paper, the mapping of appropriate BIM entities to valuation code lists are presented. In order to prove the benefits that such solution brings, an analysis of the cost-effectiveness of using BIM in the valuation and taxation process is carried out and presented.

Predictability of Belgian residential real estate rents using tree-based ML models and IML techniques

PurposeThe purpose is twofold. First, this study aims to establish that black box tree-based machine learning (ML) models have better predictive performance than a standard linear regression (LR) hedonic model for rent prediction. Second, it shows the added value of analyzing tree-based ML models with interpretable machine learning (IML) techniques.Design/methodology/approachData on Belgian residential rental properties were collected. Tree-based ML models, random forest regression and eXtreme gradient boosting regression were applied to derive rent prediction models to compare predictive performance with a LR model. Interpretations of the tree-based models regarding important factors in predicting rent were made using SHapley Additive exPlanations (SHAP) feature importance (FI) plots and SHAP summary plots.FindingsResults indicate that tree-based models perform better than a LR model for Belgian residential rent prediction. The SHAP FI plots agree that asking price, cadastral income, surface livable, number of bedrooms, number of bathrooms and variables measuring the proximity to points of interest are dominant predictors. The direction of relationships between rent and its factors is determined with SHAP summary plots. In addition to linear relationships, it emerges that nonlinear relationships exist.Originality/valueRent prediction using ML is relatively less studied than house price prediction. In addition, studying prediction models using IML techniques is relatively new in real estate economics. Moreover, to the best of the authors’ knowledge, this study is the first to derive insights of driving determinants of predicted rents from SHAP FI and SHAP summary plots.

House Rent Analysis with Linear Regression Model—— A Case Study of Six Cities in India

House rent in India has been rising significantly ever since the pandemic hit the country. In situations like this, it is important for consumers to have the concept of a reasonable rent price, as otherwise, they may suffer from landlords raising rents deliberately. To resolve this issue, a prediction model for rent prices is necessary. This study analyses rent data from six cities (Kolkata, Mumbai, Bangalore, Delhi, Chennai and Hyderabad) in India with multiple variables, including size, furnishing status, and the number of bathrooms, bedrooms, halls, and kitchens and creates a prediction model based on the data. The main analytical methods used are linear regression and logarithmic transformation. This study also includes a general factor analysis based on the data. The results suggest that this model is reasonably accurate for reference uses, but needs further improvements if it is to be used commercially.

Estudo de Risco Cardiovascular em Adolescentes (ERICA): consumo de álcool e fatores associados

To analyze factors that influence alcohol consumption by Brazilian adolescents aged 12 to 17 years from the five macro-regions of Brazil, according to sociodemographic, schooling, and family characteristics. This is a cross-sectional study with data from the Study of Cardiovascular Risks in Adolescents (ERICA). The outcome was assessed by alcohol consumption and considered the variables sex, age, ethnicity/skin color, maternal schooling level, having housemaids, number of bathrooms at home, living or not with parents, and type of school. For the analyses, the survey mode was used for complex samples. Poisson regression was performed to assess the magnitude of factors associated with alcohol consumption among adolescents. The prevalence of alcohol consumption by adolescents was 22.1%. The variables age range of 15 to 17 years, higher socioeconomic status, and living alone or with only one of the parents were factors that remained associated with alcohol consumption by adolescents regardless of their region of residence. Protective factors in alcohol consumption were associated with variables related to lower economic conditions and being of Asian or indigenous descent. The percentage of adolescents who consume alcohol is worrisome and must be tackled with public policies and health education. Understanding which factors are related to this situation contributes to practices and policies aimed to reduce its prevalence and damage to health.

Assessment of water consumption in households using statistical analysis and regression trees

• Statistical analysis and Regression Trees were applied to analyze water consumption. • Construction characteristics and resident-related variables were considered. • Water consumption was influenced by socioeconomic aspects. • Building age and construction characteristics significantly impact water consumption. Understanding the drivers of water consumption is essential for effective water resources management. This paper evaluates how socioeconomic and demographic variables, construction characteristics and behavioral aspects of residents influence domestic water consumption in the city of Joinville, Southern Brazil, analyzing data from 394 households. Statistical analysis and Univariate Regression Trees were applied to the data set. The results show that the average per capita consumption is 143.67 L/person/day, while the average household monthly water consumption is 14.53 m³/household/month. Amidst the findings in the statistical analysis, socioeconomic aspects such as number of residents, income and presence of children in the household showed an effect on water consumption, as well as building age and typology, number of bathrooms and presence of a bathtub and a swimming pool. The regression trees allowed identifying that the number of residents and area per capita are the variables with greater importance to estimate household and per capita water consumption, respectively. As a key implication of this study, the analysis presented herein provides additional empirical support for the body of knowledge on the factors that influence residential water consumption and can help decision-makers to direct actions and public policies aimed at water conservation in the built environment.

Housing economic valuation in the metropolitan area of Puebla, Mexico

Objective: The purpose of this research was to identify the sale value of homes in the metropolitan area of the city of Puebla, using the hedonic price technique to determine a model that takes into consideration relevant variables that explain the housing prices.Methodology: An overall problem is land mismanagement. The location of the homes can affect their price. A hedonic price model helps to determine the relative importance of the variables in the final price. This information provides an initial perspective to the people who are interested in acquiring a home in the study area. The model was built based on the information collected from 182 properties and it was analyzed using theSPSS software version 28.0.0.0.Results: The most significant variables that determine housing prices, which presented a multiple valuation coefficient of 64.3%. The effect of the variables on the housing value was determined by means of elasticities.Study Limitations: There were no limitations for this report.Conclusions: The following variables were significant: new home, number of bathrooms, built‐up area, private security, distance to the downtown of Puebla, and distance to a park.

TIME ON THE MARKET OF RESIDENTIAL CONDOMINIUM UNITS WITH PRE-SALE ARRANGEMENT IN COLOMBO DISTRICT

The purpose of this paper is to study the time on market (TOM) for residential condominium units under pre sales arrangements and what key factors drive that TOM. The contextual understanding of previous studies on TOM for residential properties confined for properties sold either in the secondary market or properties where construction is completed. This study focuses on the relationships among TOM, listed price, and basic property characteristics of residential condominium units sold under the provisional condominium plan in Colombo, Sri Lanka. Following non-probability sampling technique, this study examines TOM of 189 pre-sale residential condominium units in Colombo district which came into market between 2016 January to 2019 January. Developers were approached to collect required data such as transaction price, transaction date, final listed date listed price, number of bedrooms, number of bathrooms, floor area, floor level, construction completion, discounting rates likewise. Data analysis followed descriptive statistics, Pearson Correlation, and multiple regression model. The analysis highlighted that from January-2016 to January-2019, TOM of provisional residential condominiums of Colombo varied from 12.76 to 5.51 months. The results further highlighted that the TOM of a pre-sale provisional residential condominium unit is a function of floor area, number of bathrooms, level of the unit, construction completion & discount rate. The study highlights that during the period considered, the floor area units of 744sqft at the initial stage of construction with a discount rate between 10% to 20% from the listed price can predicted to have the lowest TOM..
 Keywords: Time on the Market; Pre-sale; Residential Condominium Units; Listed Price; TransactionPrice.

Study of domestic water consumption in intermittent supply of the Riberas de Sacramento sector in Chihuahua, Mexico

Abstract User water demand can be met in intermittent water supply systems, and normally it is met if they have sufficient capacity to store water in their homes to be used in the hours when it is not supplied from the public pipe network. The situation in many intermittent water supply systems is that they have enough water to cover the demand of the users but are supplying it intermittently, and the challenge is to achieve a transfer from intermittent to continuous supply. If the delivery of water is continuous, which supplies drinking water 24/7, it covers not only the basic needs of the user, but all the needs for water they may have. The present study was carried out using smart domestic water meters to obtain consumption information in an intermittently supplied pilot sector. The electromagnetic meters record the water flow through pulses and generate consumption information, which gives us the consumption pattern in volume of water and in the time it is used. During the study, multivariate statistical methods were applied to obtain information from the domestic meters and to identify, as realistically as possible, the consumption of drinking water and the relationship between consumption and specific characteristics of the users and thus anticipate future demand. The results showed a close relationship between consumption and the size of the residence. The most influential factors were the number of bathrooms and the number of occupants. A direct relationship between the pressure and the volume supplied was presumed at the beginning of the study but the opposite was found. Equipment was installed to measure the pressure in the network at the time of establishing the continuous supply of drinking water. The multivariate analysis provides a selection of the most important variables that influence and explain the behavior of the water use and consumption pattern, with the operation determined by the operating agency.

Childhood Geography: a study about Children's Satisfaction with Housing in Buenos Aires Province, Argentina.

This article presents the study of the characteristics and infrastructure of houses inhabited by children and their satisfaction with them. It embraces a sample of 580 12-year-olds children, living in the district of General Pueyrredón (PGP), an area located in the Southeast of the Province of Buenos Aires, Argentina. This survey was carried out in the framework of the research project acknowledged by the LOMASCyT Program of the National University of Lomas de Zamora, Argentina and developed by the UNICOM-Institute of Social Studies, of the School of Social Sciences. Specifically the core of this research considered the study and analysis of the following variables of children’s lives, namely: the number of rooms in their houses, the number of bathrooms in their houses, the accessibility to own or shared room and own or shared bed for sleeping, having a space of their own to study, having water service, light and sewers in their houses, and the level of satisfaction with the house they inhabit.

Does the family affluence scale reflect actual parental earned income, level of education and occupational status? A validation study using register data in Sweden

AimTo examine the external validity of the Family Affluence Scale (FAS) among adolescents in Sweden by using register data for parental earned income, level of education and occupational status.MethodsData from the baseline (2015–2019) of the Study of Adolescence Resilience and Stress (STARS), comprising 2283 13-year-olds in the region of Västra Götaland, were used. The FAS III consists of six items: unshared bedroom, car ownership, computer/tablet ownership, dishwasher, number of bathrooms and number of holidays abroad. Register data regarding earned income, educational level and occupational status from Statistics Sweden (2014–2018) were linked to adolescents. In total, survey data were available for 2280 adolescents, and register data were available for 2258 mothers and 2204 fathers.ResultsTotal parental earned income was moderately correlated with adolescents’ scoring on FAS (0.31 < r < 0.48, p < 0.001), depending on examination year. The low FAS group mainly comprised low-income households, and the high FAS group mainly comprised high-income households. Correlations between mothers’ and fathers’ educational level and adolescents’ scoring on FAS were low (r = 0.19 and r = 0.21, respectively, p < 0.001). FAS was higher among adolescents whose parents were working, but the correlation between parents’ occupational status and FAS was low (r = 0.22, p < 0.001).ConclusionsThe FAS can mainly identify low- and high-income households in Sweden. It may be used as an alternative measure of parental earned income in studies using self-reported socioeconomic status among adolescents.

Does the Family Affluence Scale reflect parental earned income, education and occupational status?

Abstract Background The Family Affluence Scale (FAS) is an alternative measure of families' socioeconomic conditions, developed for surveys to children and adolescents. It was adopted by the Health Behaviour in School-aged Children (HBSC) survey in 1993/94 and is frequently used in international research examining socioeconomic inequalities in health among children and adolescents. It has been validated in several countries, but not with register data. The aim of this study was to examine the external validity of the FAS among adolescents in Sweden, by using register data for parental earned income, level of education and occupational status. Methods Data from the baseline (2015 − 2019) of the Study of Adolescence Resilience and Stress (STARS), comprising 2 283 13-year-olds in the region of Västra Götaland, were used. The FAS III consists of six items; unshared bedroom, car ownership, computer/tablet ownership, dishwasher, number of bathrooms and number of holidays abroad. Register data from Statistics Sweden (2014 − 2018) were linked to the adolescents. Pearson's and Spearman's correlation coefficients were calculated. Results Total parental earned income was moderately correlated to adolescents' scoring on FAS (0.31 &amp;lt; r &amp;lt; 0.48, p &amp;lt; 0.01), depending on examination year. Correlations between mothers' and fathers' educational level and adolescent's scoring on FAS were low (r = 0.19 and r = 0.21 respectively, p &amp;lt; 0.01). FAS was higher among adolescents with working parents, but the correlation between parent's occupational status and FAS was low (r = 0.22, p &amp;lt; 0.01). The low FAS group mainly comprised low income households and the high FAS group mainly high income households. However, in the medium FAS group, all income quintiles were equally represented. Consequently, the medium FAS group should be used as the reference group. Conclusions The FAS was moderately associated to total parental earned income and mainly identified low income and high income households. Key messages The FAS may be used as an alternative measure of total parental earned income in studies based on self-reported socioeconomic status among adolescents. Ensuring that instruments intended to measure socioeconomic conditions are valid is crucial in public health.

Prediksi Harga Rumah Menggunakan Web Scrapping dan Machine Learning Dengan Algoritma Linear Regression

Tempat tinggal atau rumah merupakan salah satu dari banyaknya kebutuhan primer bagi manusia. Maka dari itu sangat penting untuk membuat perencanaan agar tiap keluarga dapat memiliki tempat tinggal pribadi. Dalam perencanaan tersebut dibutuhkan prediksi atau perkiraan harga di masa mendatang. Maka, tujuan penelitian ini bertujuan untuk membuat model prediksi harga rumah menggunakan metode machine learning adapun algoritmanya adalah linear regression. Dengan melakukan web scraping untuk mengumpulkan data, melalui beberapa website yang bergelut dibidang jual beli rumah. Adapun menurut developer rumah yang berhasil ditanyakan dilapangan terkait variabel yang mempengaruhi harga rumah diantaranya luas lahan, luas bangunan berdiri, banyaknya kamar tidur, banyaknya kamar mandi, dan ketersediaan tempat parkir mobil. Untuk mendapatkan nilai prediksi yang tinggi penelitian dilakukan secara berulang kali namun nilai prediksi terbesar dengan menggunakan 80% dataset untuk training dan 20% dataset digunakan untuk testing menghasilkan nilai output dengan tingkat akurasi dalam memprediksi sebesar 88%.