Number Of Bathrooms Research Articles

Factors affecting housing prices in Izmir, Turkey: a quantile regression approach

PurposeThe purpose of this paper is to investigate empirically the main factors that affect the house prices in Izmir, Turkey using the quantile regression and ordinary least square approaches.Design/methodology/approachSample data about the housing market for Izmir collected from the web pages of various real estate agencies during June 2018. Following this, the quantile regression method is used to estimate all possible effects of variables on each interested quantile to determine the factors that affect house prices to guide the potential consumers, house developers, city planners and the policymakers in Izmir, Turkey.FindingsResults show that the age of the house, central heating and parking have no significant effect on prices. The size of the house, the existence of an elevator, fire and security have a positive and significant effect on prices. The number of rooms has lower values for high-priced houses, while the floor, the number of balconies, air conditioning, proximity to schools have a higher value for high-priced houses. The number of toilets, the number of bathrooms and the distance to the hospital have a lower value on the high-priced housing. The value of the distance from the city center and the shopping center is almost uniform in all quantiles and lowers the value of the higher-priced houses. With the exception of the value of the houses in the 10th percentile in Balcova district, the value of the houses in Konak, Balcova and Narlidere is lower prices in Karsiyaka.Originality/valueThis is the first comprehensive research to determine the major factors that affect house prices in Izmir. The second contribution of this paper is that it includes all possible variables and accordingly derives adequate policy implications, which could be used both by the public housing authority and private housing constructing companies in designing and implementing effective housing policies.

Determining the Flat Sales Prices by Flat Characteristics Using Bayesian Network Models

There are various factors affecting flat sales prices. Various characteristics of a flat play an important role in determining its sales price. In this study, a machine learning based Bayesian network was built by a restrictive structural learning algorithm using the data collected from 24 randomly selected cities in Turkey. The data consist of the sales prices and various characteristics of a flat such as number of bedrooms, building age, availability of balcony, net area, heating type, mortgageability, number of bathrooms, seller type, presence in a housing estate area and floor location. After the model validity check, a sensitivity analysis was performed for the estimated Bayesian network model and related results were provided. Some of these results indicate that sales prices of flats mostly change depending on the number of bathrooms available. Additionally, number of bedrooms, net area and floor location are also determinative about the sales prices. The lack of significant difference among the sales prices of flats that are sold by construction companies, housing estate agents or property owners is another result obtained.

Comparison of real estate appraisal methodologies based on artificial intelligence

The aim of this paper is to present and compare models that estimate the value of a property according to its characteristics. This information can be required by, for example, potential investors in the real estate markett. In this way, they will be able to compare the offer price of the property with the value obtained by the model, before making investment or disinvestment decisions. In this paper we apply supervised (regression tree, random forerst, nearest neighbour, SVM) and unsupervised (clustering) data analysis methods in order to estimate the value of dwellings located in the city of Madrid, Spain. It is found that the housing characteristic that most influences the price is the surface area, and to a lesser extent the number of rooms, whether the building has a lift, the number of bathrooms and whether the building has a car park. The best method of estimation, according to the MAPE, was the random forest method.

The politics of urban trees: Tree planting is associated with gentrification in Portland, Oregon

This study evaluated the hypothesis that urban-tree planting increases neighborhood gentrification in Portland, Oregon. We defined gentrification as an increase in the median sales price of single-family homes in a Census tract compared to other tracts in the city after accounting for differences in the housing stock such as house size and number of bathrooms. We used tree-planting data from the non-profit Friends of Trees, who have planted 57,985 yard and street trees in Portland (1990–2019). We estimated a mixed model of gentrification (30 years and 141 tracts) including random intercepts at the tract level and a first-order auto-regressive residual structure. Tract-level house prices and tree planting may be codetermined. Therefore, to address potential endogeneity of tree planting in statistical modeling, we lagged the number of trees planted by at least one year. We found that the number of trees planted in a tract was significantly associated with a higher tract-level median sales price, although it took at least six years for this relationship to emerge. Specifically, each tree was associated with a $131 (95% CI: $53–$210; p-value = 0.001) increase in tract-level median sales price six years after planting. The magnitude of the association between the number of trees planted and median sales price generally increased as the time lag lengthened. After twelve years, each tree was associated with a $265 (95% CI: $151–$379; p-value<0.001) increase in tract-level median sales price. Tree planting was not merely a proxy for existing tree cover, as the percent of a tract covered in tree canopy was independently associated with an increase in median sales price. Specifically, each 1-percentage point increase in tree-canopy cover was associated with a $882 (95% CI: $226–$1538; p-value = 0.008) increase in median sales price. In conclusion, tree planting is associated with neighborhood-level gentrification, although the magnitude of the association is modest.

IMPLEMENTASI METODE SIMPLE ADDITIVE WEIGHTING (SAW) PADA SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN RUMAH BERBASIS ANDROID

The process of selecting house is a difficult thing to do, because there are many alternatives offered by developers. The purpose of this research is to build an Android-based decision support system that can help selection process involving ten criteria. The criteria used in this research are house prices, building area, land area, electricity, water sources, distance from market, distance from university, number of bedrooms, number of bathrooms, and number of floors. The system built applies Simple Additive Weighting (SAW) method. SAW is one of the well-known methods and is often used for multiple attribute decision making because of its simplicity in providing alternatives during the decision making process. Based on system performance testing, the results of accuracy is 85,7%, which at this time has been categorized as very good. Based on the results of alpha testing and beta testing, it can be concluded that application has worked and meets the level of user satisfaction, with value that is categorized as very good

The relationship of 25-hydroxyvitamin D concentrations and individual-level socioeconomic status

Socioeconomic status (SES), defined as the ability to access desired resources, is associated with behaviors that may affect vitamin D status. Most studies of the effect of vitamin D status on outcomes do not account for individual-level SES. The ability to adjust for SES in epidemiologic studies, when data on conventional SES measures have not been obtained, would be advantageous. We identified all serum 25(OH)D measurements in adults age 18 years and older residing in Olmsted County, MN, a mixed urban-rural setting, between January 1, 2005 and December 31, 2011, through the Rochester Epidemiology Project. The first 25(OH)D measurement was considered the index measurement for each subject. SES was determined for each subject by the HOUsing-based SocioEconomic Status (HOUSES) index, derived from real property data. The HOUSES index is an aggregated z-score of assessed housing value, area of living space, number of bedrooms, and number of bathrooms, with higher scores indicating higher SES. Multivariable analyses were adjusted for age, BMI, sex, race, season of 25(OH)D measurement, and Charlson comorbidity index. HOUSES was matched for 10,378 of 11,002 subjects (94 %) with 25(OH)D measurements available. The mean (SD) age was 54.3 (17.1) years with 26.9 % ≥65 years; 77.3 % were women, and 12.1 % were non-white. The mean 25(OH)D concentration was 30.0 (12.9) ng/mL, and 598 (5.8 %) had a 25(OH)D value <12 ng/mL. The mean (SD) HOUSES was -1.55 (3.09),-0.97 (3.34), 0.14 (3.52), 0.24 (3.51) for serum 25(OH)D categories of <12, 12–19, 20–50, and >50 ng/mL, respectively (P = 0.12 for trend). 25(OH)D increased by 0.43 (95 % CI 0.36-0.50) ng/mL for each unit increase in HOUSES in univariate analysis and by 0.28 (0.21-0.35; P < 0.001) ng/mL in multivariable analysis. This represents a change of 4 ng/mL across the entire range of observed HOUSES, an effect similar in magnitude to the seasonal variation of 25(OH)D values. SES was independently associated with serum 25(OH)D concentrations in a dose-response manner after adjustment for important covariates. HOUSES is a useful tool to assess the role of individual-level SES in health outcomes when other SES measures are unavailable and to control for confounding by SES in examining the effect of 25(OH)D on clinical and metabolic outcomes.

Identifying the Drivers of Water Consumption in Single-Family Households in Joinville, Southern Brazil

This study aims to identify the factors that may influence water consumption in single-family households in the city of Joinville, Southern Brazil. Through questionnaires, data were collected from 108 households in several neighborhoods of the city. The questionnaires contained open-ended and closed-ended questions involving the surrounding infrastructure, socio-economic and demographic characteristics, constructive characteristics, installed plumbing fixtures, and water-use habits, totaling 57 variables. The independent variables were correlated to monthly water consumption (m3/month/household) and per capita consumption (liters/person/day) of each household. The statistically significant variables that affected households water consumption were related to demographic characteristics such as number of residents and educational level, construction features (i.e., number of bathrooms, building age, and built area), the presence of water-efficient appliances and water conservation habits. The results obtained can contribute to the development of new studies on water consumption and sustainable policies and awareness on the importance of water conservation.

Optimasi Metode Particle Swarm Optimization (PSO) Pada Prediksi Penilaian Apartemen

One property that is currently being glimpsed by investors is an apartment. Property consulting companies as one of the service provider companies that become a link between apartment owners and apartment enthusiasts, have an important task in terms of providing information about the assessment of the offered institutions. This study will conduct a trial on the accuracy of apartment assessment predictions using the Support Vector Machine (SVM) method, then will be compared again with the results of the accuracy of the assessment method Support Vector Machine (SVM) combined using the optimization method Particle Swarm Optimization (PSO). The results of the combination of the application of SVM and PSO are used to optimize attribute selection in apartment valuation to improve the accuracy of using the SVM method. This study shows that the Particle Swarm Optimization (PSO) Support Vector Machine (SVM) method is a pretty good method of data classification, because it can seen from the increase in accuracy of 2.84% and AUC of 0.003. Subjects (attributes) that affect apartment valuation are seen from rent prices (price range), city (apartment location), size (area), furnisihing (equipment), bedroom (number of bedrooms), bathroom (number of bathrooms) and maids badroom (number of maid rooms). The results of the attribute testing showed that city attributes (apartment locations), furnisihing (equipment) and maid badroom (number of maid rooms) greatly influenced the valuation of an apartment.

Listing price estimation of apartments: A generalised linear model

Orientation: Residential property is an important segment of the property market in South Africa. Residential property transactions are typically infrequent and relate to a highly differentiated set of items making measurement techniques complex and difficult. Research purpose: The aim of this research was to develop a statistical model to estimate listing prices of apartments in KwaZulu-Natal, South Africa, and build a software application to disseminate the results thereof. Motivation for the study: This study presents a novel alternative to the log linear (ordinary least squares) method of deriving a hedonic price function for residential property where the arithmetic mean is computed as the expected value and not the geometric mean. Research design, approach and method: Using a data set of 1314 residential apartments provided by Private Property (Pty) Ltd, this research derives a hedonic price function for residential property using a generalised linear model based on the gamma distribution and log-link function. Main findings: The results showed that floor area, number of bedrooms, number of bathrooms and a dummy variable for suburb (location) were statistically significant determinants of listing prices. Practical/managerial implications: A software application, called the listing price calculator , was developed to disseminate the results of the model for commercial use by real estate buyers, sellers and agents, bridging the gap between academia and business. Contribution/value-add: This study derives a hedonic price function for residential property using a generalised linear model based on the gamma distribution and log-link function, which is novel in South African research.

Driving forces for the US residential housing price: a predictive analysis

PurposeThe purpose of this paper is to determine the key characteristics that determine housing prices in the USA. Data analytical models capable of predicting the driving forces of housing prices can be extremely useful in the built environment and real estate decision-making processes.Design/methodology/approachA data set of 13,771 houses is extracted from the 2013 American Housing Survey (AHS) data and used to develop a Hedonic Pricing Method (HPM). Besides, a data set of 22 houses in the city of San Francisco, CA is extracted from Redfin real estate brokerage database and used to test and validate the model. A correlation analysis is performed and a stepwise regression model is developed. Also, the best subsets regression model is selected to be used in HPM and a semi-log HPM is proposed to reduce the problem of heteroscedasticity.FindingsResults show that the main driving force for housing transaction price in the USA is the square footage of the unit, followed by its location, and its number of bathrooms and bedrooms. The results also show that the impact of neighborhood characteristics (such as distance to open spaces and business centers) on the housing prices is not as strong as the impact of housing unit characteristics and location characteristics.Research limitations/implicationsAn important limitation of this study is the lack of detailed housing attribute variables in the AHS data set. The accuracy of the prediction model could be increased by having a greater number of information regarding neighborhood and regional characteristics. Also, considering the macro business environment such as the inflation rate, the interest rates, the supply and demand for housing, and the unemployment rates, among others could increase the accuracy of the model. The authors hope that the presented study spurs additional research into this topic for further investigation.Practical implicationsThe developed framework which is capable of predicting the driving forces of housing prices and predict the market values based on those factors could be useful in the built environment and real estate decision-making processes. Researchers can also build upon the developed framework to develop more sophisticated predictive models that benefit from a more diverse set of factors.Social implicationsFinally, predictive models of housing price can help develop user-friendly interfaces and mobile applications for home buyers to better evaluate their purchase choices.Originality/valueIdentification of the key driving forces that determine housing prices on real-world data from the 2013 AHS, and development of a prediction model for housing prices based on the studied data have made the presented research original and unique.

Access to homebuyer credit and housing satisfaction among households buying affordable apartments in urban Vietnam

This study examines the relationship between the access to homebuyer credits and housing satisfaction among those buying affordable apartments, using a sample of 1,000 respondents from our own survey in 2016 in Hanoi, Da Nang and Ho Chi Minh Cities. Our regression analysis reveals the education level, the size and value of apartments are closely linked with the access to preferential homebuyer credits. Notably, we find that the access to preferential home loans has a strongly positive impact on housing satisfaction, after controlling for all other factors in the model. Thus, the finding confirms that preferential home loan programs play an important role in helping low-income households own affordable apartments and increase their housing satisfation. We also find that some other features of their apartments, such as the number of bathrooms and balconies, the distance from the apartment building to schools, bus stations and markets, are strongly linked with housing satisfaction.

Towards a Hedonic Pricing Method for the Bucharest Private Housing Market

This paper aims at exploring the drivers of dwellings’sale prices in Bucharest, Romania, over the period 2014-2018. Several housing structural attributes are covered, such as the number of rooms, useful and constructed surface, type of comfort, floor, the number of bathrooms, balconies and parking, the seniority from construction, as well as from renovation, structure type, height regime, and the duration until completion of the real estate transaction. By estimating a standard hedonic price model via OLS regression for a sample of 765 transactions, we notice that all the selected variables, except the floor level and seniority from construction, positively influence the property prices. However, in case of useful and constructed surface, nonlinear relationships with property prices were acknowledged. Robustness checks in form of quantile regressions reinforce the empirical findings.

Tuberculosis incidence inequalities and its social determinants in Manaus from 2007 to 2016

BackgroundBrazil is among the 30 countries with high-burden of tuberculosis worldwide, and Manaus is the capital with the highest tuberculosis incidence. The accelerated economic and population growth in Manaus in the last 30 years has strengthened the process of social stratification that may result in population groups that are less favored in terms of healthcare and are vulnerable to infection and illness due to tuberculosis. This study aimed to characterize inequalities associated with tuberculosis incidence in relation to the socioeconomic and demographic characteristics of the resident population of Manaus and to identify their determinants from 2007 to 2016.MethodsAn ecological study was conducted using the data from the Diseases Notification Information System. Tuberculosis incidence rates by population characteristics (gender, ethnicity, and socioeconomic level) were calculated for each year, studied, and represented in equiplot charts. To measure the disparity of tuberculosis incidence in the resident population in Manaus, the Gini index of tuberculosis in each neighborhood was calculated based on the incidence rates of the census sectors. A thematic map was constructed to represent the spatial distribution of tuberculosis incidence inequality. Linear regression models were used to identify the relationship between the tuberculosis incidence inequality and its social determinants.ResultsFrom 2007 to 2016, there was an increase in the tuberculosis incidence in Manaus, together with an increase in incident inequality among genders, ethnic groups, and socioeconomic level. The incidence of tuberculosis inequality was associated with the inequalities of its possible determinants (Gini of the proportion of male population, Gini of the proportion of indigenous population, Gini of the proportion of illiteracy, Gini of income, Gini of the proportion of households connected to the water network, and Gini of the mean number of bathrooms per inhabitant), the per capita income, and the proportion of cases with laboratory confirmation.ConclusionsDisparities in tuberculosis incidence in the resident population in neighborhoods can be explained by the sociodemographic and economic heterogeneity. Our findings recommend that public policies and tuberculosis control strategies consider differences in the determinants of tuberculosis inequality for the development of specific actions for each population group.

Prediction of residential gross yields by using a deep learning method on large scale data processing framework

Purpose- Households, investors and companies who want to make an investment on residential properties are interested in sales prices and rental values that vary depending on regional factors, location and attributes of residential units. It is the preference of investors to buy a new house with higher rental income. Real estate developers and real estate consultants as well as the real estate investors are also interested in investigating relationship between gross yield rate and location, regional factors, attributes of residential units. The purpose of this study is to examine the relationship between attributes of the residential units, location of the units and the gross yield rate. Methodology - In this study, the prediction model of residential gross yield rates with the help of city, county, district, residential attributes information, was created by using LSTM, which is a deep learning method, on big data platform Spark. Findings- According to test results, it has been proven that gross yield rates could be estimated with high accurate model by the aid of Long short term memories. With this model, researchers can predict gross yield rate of any specific flat. Conclusion- The LSTM network has been built in this study shows that the residential gross yield rate could be estimated using city, county, district, number of rooms, number of bathrooms, floor number, total floor attributes. This study also shows that the Spark framework can be used to deal with the growing size of data in real estate and to develop deep learning applications on distributed data processing platforms.

Analysis of domestic water demand variables of a residential colony in Ajmer, Rajasthan (India)

Abstract In this paper, significant variables of domestic urban water demand required for the purpose of estimation of urban water supply in five planned colonies of the City of Ajmer, Rajasthan, India, are identified. The data for these 16 variables are entered in the multiple linear regression (MLR) (stepwise) models in SPSS software, and domestic water demand models are developed. Based on these models, the six most significant variables, namely temperature (T), rainfall (RF), family size (FS), family income (FI), number of bathrooms (NB), and age of house (AH), are identified. The data of 16 variables are further utilized in principal component analysis (PCA), and five factors/variables are extracted, comprising combinations of these 16 variables. A regression coefficient of 0.76 is obtained in the PCA model. These six significant variables are further fed into a multilayer perceptron neural network (NN) model for water demand forecasting. The linear regression coefficient of NN is 0.90, very close to the MLR (stepwise) coefficient of 0.89, and verifying the dependence of water demand on these six variables. The outcome of the study suggests that the six variables are significant for estimation of water demand for Ajmer.

Quadratic Discriminant Analysis of Dengue Viruses Disease Incidence in Palembang

Palembang is classified as a city that has tropical climate with abundant rainfall, even in the driest month. These characteristics indicate that the city has a high potential for the occurrence of dengue virus disease. The areas of incidence of dengue virus disease in Palembang can be classified into five areas; South, Center, North, West and East. The aim of this paper is to map the incidence of dengue virus disease into these areas based on significant factors using the quadratic discriminant analysis. The stratified sampling technique is applied to obtain the respondents who have family members suffering from dengue disease. The results showed that the incidence of dengue virus disease in each area is significantly affected by all factors, except the age of patient, hygienic behavior of respondent, age and occupation of family head and number of bedrooms. The results of the Wilk’s lambda test and the F test indicate that gender and blood type of patient; age, education, occupation, income and knowledge of respondent; occupation and income of family head; type of residence; number of bathrooms that have tubs and clean water source are relatively different in each area. The overall correct percentage of the mapping result is 66.7%.

Determinants of domestic water consumption in Hermosillo, Sonora, Mexico

This article presents a cross-sectional study that explored the determinants of domestic water consumption in Hermosillo, Mexico in a continuous 24-h water supply. A probability sample with a confidence interval (CI) of 95% was established by applying a 65 question survey to collect data on 403 households. Consecutively, the model reflecting the water consumption in Hermosillo households was identified through a multiple regression analysis using ordinary least squares (OLS). The analysis revealed that the statistically significant variables that explain the domestic water consumption in the home with a share of 31.6% were low water cost, number of bathrooms in the household, measured service supply, use of purified bottled water per week in households, and the number of female inhabitants. After obtaining the model, the monthly water consumption was calculated in a group of houses throughout the equation to compare it with the water consumption measured by the operating agency, and it showed an acceptable approximation (78–90%). The results contribute to improving the current understanding of factors influencing the use of the vital liquid, and they may be useful in the development of policies to promote the sustainable use of water resources.

Assessing health disparities in children using a modified housing-related socioeconomic status measure: a cross-sectional study

ObjectivesSocioeconomic status (SES) is a well-established risk factor for many health outcomes. Recently, we developed an SES measure based on 4 housing-related characteristics (termed HOUSES) and demonstrated its ability to...

A comprehensive dwelling unit choice model accommodating psychological constructs within a search strategy for consideration set formation

This study adopts a dwelling unit level of analysis and considers a probabilistic choice set generation approach for residential choice modeling. In doing so, we accommodate the fact that housing choices involve both characteristics of the dwelling unit and its location, while also mimicking the search process that underlies housing decisions. In particular, we model a complete range of dwelling unit choices that include tenure type (rent or own), housing type (single family detached, single family attached, or apartment complex), number of bedrooms, number of bathrooms, number of storeys (one or multiple), square footage of the house, lot size, housing costs, density of residential neighborhood, and commute distance. Bhat’s (2015) generalized heterogeneous data model (GHDM) system is used to accommodate the different types of dependent outcomes associated with housing choices, while capturing jointness caused by unobserved factors. The proposed analytic framework is applied to study housing choices using data derived from the 2009 American Housing Survey (AHS), sponsored by the Department of Housing and Urban Development (HUD) and conducted by the U.S. Census Bureau. The results confirm the jointness in housing choices, and indicate the superiority of a choice set formation model relative to a model that assumes the availability of all dwelling unit alternatives in the choice set.

House Rent Estimation in Dhaka City by Multi Layer Perceptions Neural Network

This paper attempts to build an artificial neural network that can estimate the median price of a home in a neighborhood described by forty demographic attributes in areas of Dhaka City namely Dhanmondi, Baridhara, Gulshan, Mirpur, Uttara and Old Dhaka. A structured questionnaire was used to collect the relevant data and the housing data sets was used to develop constant quality price indices using traditional econometric techniques and using neural networks incorporating genetic algorithms. Factors including house size, house age, house type, number of bedrooms, number of bathrooms, number of garages, amenities around the house and geographical location are considered. The analysis indicates that neural networks act as real alternative to the econometric methods. In this paper thirteen conditional attributes have been considered to estimate the house rent. This paper provides some indicative policy guidelines to handle the house rent problem in the Dhaka City and suggests that a rent controller should be appointed for each ward and maximum rents for particular areas should be gazette and the rents should be paid through banks.