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Modelling Residential House Pricing Using Regression Analysis
Abstract
The paramount ingredient for the socio-economic development of any country is the land and buildings. An accurate appraisal of these land and buildings’ value has a colossal effect on the state’s economy. Still, the value of these buildings has been outrageous with realtors’ emergence. Various computational techniques have been employed to resolve the issue of the appraisal of the value. The factor analysis, correlation analysis and linear regression analysis are employed in this paper in order to model the residential house prices. This study is to be carried out in the Chengalpattu neighbourhood, where the modelling of the residential house prices is considered. All the factors which account for house price were determined. The modelling is performed by taking the market prices and the various factors that account for the valuation using various software.KeywordsHouse pricingRegressionUrban density
Urbanisation influences resource use through increasing affluence and dense infrastructure, enabling different lifestyles. Previous literature has highlighted the benefits of material efficiency that cities can provide. However, there is little research on household material footprints (MFs) in this context. MFs cover all material requirements throughout global supply chains and allocate these to the final consumer of a product or service. The study aims to fill this literature gap by analysing how household MFs and their different categories vary in Europe according to socioeconomic variables and the degree of urbanisation. Furthermore, we adjust our baseline model to see how adding the MF of transport infrastructure use affects the outcomes. The MFs decrease strongly with an increasing degree of urbanisation when infrastructure is taken into account, but much less so, when only household material consumption is considered. Surprisingly, household metal footprints are higher in rural than urban areas. This is mainly because household vehicles have a larger impact on metal footprints than electronic devices, for example. Differences in the MF patterns between countries highlight the importance of tailored material efficiency policies considering local characteristics.
In this research we analyse the most important factors that determine housing prices. On the one hand, we test whether neighbourhoods with a good accessibility are more attractive and consequently show higher housing prices. For this purpose, we introduce an adapted Walk Score as part of the accessibility indicators. On the other hand, we compare an ordinary-least-squares regression (OLS) and a spatial lag model and test which model better explains residential housing prices. The regression models show the importance of classical factors such as dwelling characteristics or the types of neighbours. In addition, they also reveal that a differentiated approach is needed for analysing the accessibility, the location and the environment of a dwelling. The mere presence of a single amenity, a public transport stop or a motorway access is not a sufficient explanatory factor. Information such as density of supply, walking distances or public transport service quality needs to be taken into account as well as. The test of the spatial lag model reveals that prices of the most proximate dwellings can be taken into account as a relevant factor in explaining residential housing prices and should therefore be included in research on residential housing prices.
The Hedonic Model, a traditional method for forecasting house prices has been criticised due to nonlinearity, multicollinearity and heteroskedasticity problems, which were argued to affect estimation accuracy. Unlike the Hedonic Model, the Artificial Neural Network Model (ANN), permits nonlinear relationships and thus avoids the problems plaguing the Hedonic Model resulting in superior forecasting performance. Despite these advantages, attempts to model house prices using ANN are limited in geography and data thus besetting the usefulness of the results. To address the research gap, this paper aims to establish such a new model using ANN in forecasting house prices. A sample of double-storey terraced houses transacted in Johor Bahru are analysed using ANN. The findings illustrate a superior forecasting performance for ANN through high values of goodness of fit and low values of errors. This paper adds to the house price modelling literature and provides new knowledge to both academics and practitioners.
This is a survey of neural network applications in the real-world scenario. It
provides a taxonomy of artificial neural networks (ANNs) and furnish the reader
with knowledge of current and emerging trends in ANN applications research
and area of focus for researchers. Additionally, the study presents ANN
application challenges, contributions, compare performances and critiques
methods. The study covers many applications of ANN techniques in various
disciplines which include computing, science, engineering, medicine,
environmental, agriculture, mining, technology, climate, business, arts, and
nanotechnology, etc. The study assesses ANN contributions, compare
performances and critiques methods. The study found that neural-network models
such as feedforward and feedback propagation artificial neural networks are
performing better in its application to human problems. Therefore, we proposed
feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance,
volume, scalability, convergence, and performance. Moreover, we recommend
that instead of applying a single method, future research can focus on combining
ANN models into one network-wide application.
Linear regression is a statistical procedure for calculating the value of a dependent variable from an independent variable. Linear regression measures the association between two variables. It is a modeling technique where a dependent variable is predicted based on one or more independent variables. Linear regression analysis is the most widely used of all statistical techniques. This article explains the basic concepts and explains how we can do linear regression calculations in SPSS and excel.
Correlation in the broadest sense is a measure of an association between variables. In correlated data, the change in the magnitude of 1 variable is associated with a change in the magnitude of another variable, either in the same (positive correlation) or in the opposite (negative correlation) direction. Most often, the term correlation is used in the context of a linear relationship between 2 continuous variables and expressed as Pearson product-moment correlation. The Pearson correlation coefficient is typically used for jointly normally distributed data (data that follow a bivariate normal distribution). For nonnormally distributed continuous data, for ordinal data, or for data with relevant outliers, a Spearman rank correlation can be used as a measure of a monotonic association. Both correlation coefficients are scaled such that they range from -1 to +1, where 0 indicates that there is no linear or monotonic association, and the relationship gets stronger and ultimately approaches a straight line (Pearson correlation) or a constantly increasing or decreasing curve (Spearman correlation) as the coefficient approaches an absolute value of 1. Hypothesis tests and confidence intervals can be used to address the statistical significance of the results and to estimate the strength of the relationship in the population from which the data were sampled. The aim of this tutorial is to guide researchers and clinicians in the appropriate use and interpretation of correlation coefficients.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
The Artificial Neural Network (ANN) technique has been applied and found useful for solving forecasting problems in different property markets around the world. However, the trend of ANN's application in property valuation research has been undocumented. Therefore, this study aims to systematically review the extant literature on the application of ANN to property valuation. The analysis of the retrieved articles revealed that a seminal study in this area was reported in 1991. To date, the technique has continued to gain popularity amongst real estate researchers. Most of the reviewed articles originated from developed countries, particularly the US and the UK, although a few studies emanated from emerging economies. Most of the authors that contributed to the publications are affiliated to university faculties and most of the studies found ANN to have outperformed other appraisal techniques, in terms of accuracy. The gaps identified in this study need to be addressed in order to achieve sustained growth in property appraisal practice on a global scale.
This paper investigates the applicability of the hedonic approach for creating indices of residential real estate values from state land registration and valuation systems in Ghana, Africa. Using data on 2,950 transactions from five classified locations in Accra and Tema for 1992-2005, sale prices are modeled, first, for the aggregate sample, before segmenting the market on the basis of two sub-periods (1992-1998 and 1999-2005) and three price bands. A series of continuous and categorical dummy variables are included in the model to construct a residential real estate index. The main conclusion is that heterogeneous real estate characteristics such as the number of bedrooms, number of stories, real estate size and type, quality of landscaping, plot size, security of tenure, and location are all significant variables that influence real estate prices in Ghana.
This paper focuses on the modelling and forecasting of land price in Chennai Metropolitan Area (CMA) in the state of Tamilnadu, India using multiple regression and neural network techniques. Thirteen locations spread over CMA are selected at random as study areas. The monthly average values of the selected factors from the year 1997 to 2011 are considered to develop the models. Both multiple regression and neural network models are validated with the market price in the year 2012 and 2013. After validation the models are used to forecast the land price in CMA for the years 2014 and 2015. Both the models are found to be well fit for the trend of land price; however the model using neural network shows better accuracy. A careful examination of the results of forecasting bring to lime light the surge in growth of land prices in the southern and western parts of CMA.
It is well-known from the literature that locational externalities influence the price formation of residential property. This effect is usually studied empirically with the hedonic price models, by including various neighbourhood and proximity variables in the model. These regression based techniques have, however, been criticised for a number of reasons. The arguments pertain partly to technical issues such as model flexibility, functional discontinuity and nonlinearity, and data quality, and partly to more fundamental problems regarding the nature of the value formation process. The criticism has attracted experiments with new modelling approaches, each of which adds something substantial to the hedonic approach. The study comprises two parts: it first highlights the rationale behind each broad approach composed of specific modelling techniques currently available, and then demonstrates an improvement of the demand side analysis by applying the analytic hierarchy process. This method enables quantification of qualitative expert judgements, and may lead to conclusions that go beyond the purely economic value framework.
Introduces the theory of artificial neural networks (ANN).
Discusses its application to the valuation of residential apartments.
Compares the performance of the back propagation neural network (BP)
model in estimating sale prices of apartments against the traditional
multiple regression analysis (MRA) model. Concludes that the neural
network model is an easy-to-use, black-box alternative to the MRA model.
This paper analyses the housing market and its structure in Málaga, a medium-size European city, which has become one of the main Spanish tourist destinations and thus one of the most active housing markets in Spain. This has led to coastal land being misused to the extent that it has almost been used up. First, the market as a whole is studied using the hedonic methodology. Then a method is presented which segments the market into sub-markets, following an ad hoc design based on two criteria: structural (vertical and horizontal dwellings) and location (proximity to the coast). The results show that this segmentation method is efficient, demonstrating implicit attribute prices as well as final house prices which are statistically different from each other, above all when dwellings are next to the coast and between those further away. Finally, some implications for urban policy are drawn.
This paper presents an overview of two Artificial Intelligent (AI) systems, Case-Based Reasoning (CBR) and Neural Network (NN), in terms of its underlying foundation, its current state-of-the-art, and future trends in real estate industry. The use of AI systems for real estate valuation is
becoming a better alternative compared to the traditional Multiple Regression Analysis (MRA). Even if there are a number of AI systems, CBR and NN are the ones presently applied for real estate valuation. Thus this paper presents the current trends of CBR and NN in real estate valuation. In
addition, prediction capability of NN is analyzed compared to MRA. Furthermore, the benefits of integrating two AI systems for efficient real estate valuation are presented. This integration mitigates the limitations and takes advantage of the opportunities to produce systems that are more powerful than those that could be built with single intelligent systems. Therefore using hybrid intelligent systems for efficient and most accurate real estate valuation is proposed.
The objective of this study is to empirically compare the predictive power of the hedonic model with an artificial neural network model on house price prediction. A sample of 200 houses in Christchurch, New Zealand is randomly selected from the Harcourt website. 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. Empirical results support the potential of artificial neural network on house price prediction, although previous studies have commented on its black box nature and achieved different conclusions.
The metropolitan area of Chennai, India, presents an interesting case study on India’s transforming economy because it has a unique urban structure for an Indian city of its size. It has an extremely high population density at the city center that is becoming even more crowded. It is also experiencing rapid, but low-density, expansion at the periphery. This paper documents Chennai’s spatial development with detailed data on land use, population density, and land values. A hedonic regression on the price of land suggests that de facto policy differences between political jurisdictions have had a significant effect on land prices. However, the data presented in this paper suggest that land policy reforms in Chennai have been successful in reducing some of the sprawling urban development patterns evident in the 1970’s and 1980’s. Nevertheless, policymakers in Chennai continue to face the double challenge of an extremely dense urban core combined with extensive urban growth.
OLS regression has typically been used in housing research to determine the relationship of a particular housing characteristic
with selling price. Results differ across studies, not only in terms of size of OLS coefficients and statistical significance,
but sometimes in direction of effect. This study suggests that some of the observed variation in the estimated prices of housing
characteristics may reflect the fact that characteristics are not priced the same across a given distribution of house prices.
To examine this issue, this study uses quantile regression, with and without accounting for spatial autocorrecation, to identify
the coefficients of a large set of diverse variables across different quantiles. The results show that purchasers of higher-priced
homes value certain housing characteristics such as square footage and the number of bathrooms differently from buyers of
lower-priced homes. Other variables such as age are also shown to vary across the distribution of house prices.
This article is the winner of the Apartments manuscript prize (sponsored by the National Multi Housing Council) presented at the 2002 American Real Estate Society Annual Meeting.
This article applies hedonic modeling techniques to estimate the value of a sample of apartment properties sold in the greater Portland, Oregon area. An equation for value as a function of location, amenities and capitalization rate is derived. This model explains about 95% of the variation in apartment property prices. Property values decline with increasing distance from the city center. Moreover, values are shown to rise less than proportionally to increases in project size and numbers of units and decline with project age, but the marginal effect of project aging is small.
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.
When using sales comparison approach, it is crucial to apply adjustments based on the differences between the appraised and comparable properties. Estimating the significance of each adjustment is of key importance for performing credible valuation. Adjustments should include factors such as location, physical and functional characteristics of residential properties and external influential factors. The aim of the article is to determine importance of influential factors and to quantify adjustments that arise from selected factors. Analytical results about the importance of factors influencing the appraised value of residential properties as perceived by different experts are presented in the empirical part of the article. The following factors were included in the study: orientation, views from the property, noise, number of storeys, and the age of the building. Data needed in the study were collected through interviews with authorised real estate appraisers, court sworn appraisers and real property agents. The key scientific problem was to determine mathematical functions for adjustment that arise from the storey of an apartment and from the age of the building. The findings of the study indicate that the location in relation to the distance from the city centre is the most important influential factor on the property value, similarly as in other countries across the world. Furthermore, the survey results show that the impact of the views and noise on the residential property value in Slovenia is also comparable with other studies. Results on the quantification of adjustments expectedly show that prices rise per storey in case there is an elevator in the building, but the result is opposite in case there is no elevator in the building. Results also show the influence of age on the value of the building. Based on mean values a linear function of annual depreciation rate has been set, which is the key novelty of the research. This function and the adjustments presented in the article are usable in valuation practice. © 2017, Kauno Technologijos Universitetas. All rights reserved.
Unreliable and inaccurate property valuation has been associated with techniques currently used in property valuation. A possible explanation for these findings may be due to the utilisation of traditional valuation methods. In the current study, an artificial neural network (ANN) is applied in property valuation using the Lagos metropolis property market as a representative case. Property sales transactions data (11 property attributes and property value) were collected from registered real estate firms operating in Lagos, Nigeria. The result shows that the ANN model possesses a good predictive ability, implying that it is suitable and reliable for property valuation. The relative importance analysis conducted on the property attributes revealed that the number of servants’ quarters is the most important attribute affecting property values. The findings suggest that the ANN model could be used as a tool by real estate stakeholders, especially valuers and researchers for property valuation.
The price of a housing unit, as a heterogeneous and multidimensional commodity, is a function of the physical, environmental and locational characteristics. In this regard, this research was conducted to study the factors that influence the price of residential properties in Hashtgerd New Town (35o 34'48'N, 50o 26'24'E). The information required for this study was gathered from 416 households in Hashtgerd New Town. The housing pricing function was estimated by applying the Hedonic Pricing Model based on Ordinary Least Squares (OLS) method. The findings suggest that the most significant features of a housing unit include gross floor area, type of housing unit, stone facade, land area, number of rooms, number of floors and unit's distance from down town and park. Among these variables, gross floor area, type of housing unit (in terms of being a villa or an apartment) and stone facade with 0.60, 0.42 and 0.25 rates of elasticity, respectively have had the greatest positive impact on setting the price of a housing unit.
The hedonic pricing model has been employed with a degree of success in housing market analysis in developed countries.
This paper demonstrates the potentials of the same technique to the study of housing markets in a developing country like Nigeria. The
paper used data from Ikeja area of Lagos state, Nigeria, to provide empirical evidence on market parameters that describe the hedonic
price structure for apartments (flats) in a typical Nigerian city. Primary data were elicited through structured questionnaire, complemented
with selective interviews and personal observations. The results revealed that number of bedrooms, condition of the property, availability
of pipe-borne water, average size of bedrooms, and numbers of bath/toilets, in that order, are the main descriptors of apartment rentals
in the study area. The results of this empirical investigation are of particular importance to investors, developers, financiers, and real
estate valuers operating in the property sub-market under consideration. Among others, adequate knowledge of issues investigated and
raised would assist developers to build to consumer tastes and preferences. The resulting model also provides an alternative to
traditional valuation techniques and affords greater flexibility in accounting for sustainability in real estate valuation.
Purpose
– This study aims to address the factors or attributes that would influence the price of residential products in Jakarta Metropolitan Region.
Design/methodology/approach
– In total, 202 respondents from all across Jakarta Metropolitan Region participated in the questionnaire for this study. Demographic questions are categorized into age, gender and preferences for real estate locations. The questionnaire was made based on the author’s previous studies. Of the total respondents, 127 were males and 75 were females with age ranging from 18 to 56 years old. For data analysis, the authors utilized factor analysis, Cronbach’ α test and analysis of correlation to reach the conclusion of this study.
Findings
– The findings suggested that from the initial three factors groups, there are five new groups that emerge as influencing factors for housing prices. Cronbach’ α score were verified (α = 0.906). Correlation study result suggested that the initial three factors groups produce a significant correlation between each of them, except for the factor of “overall location” and “located near family.” After factor analysis, the research results show that there are two new additional groups of factors that emerge as influences to housing prices. There are significant scores of differences between gender and real estate location preference toward the groups of factors.
Research limitations/implications
– This study shows how physical qualities, concept and location factors influence the housing price perception of their consumers. The result shows to be relatively reliable and valid.
Originality/value
– The study is the first to analyze the relationship between the factors for preferences on residential products and housing price in Indonesia. This paper is also intended to be the first to pioneer the study on factors of preferences on residential products in Indonesia. The findings will be useful to develop pricing models for housing product in Indonesia.
Quite a large number of housing studies have been estimated with hedonic pricing methodology with majority of these studies imposing a priori restrictions on the functional form. However, estimating the composite housing price through imposition of a prior restriction may lead to model misspecification, erroneous and inconsistent estimates. In specifying a model, it is germane to derive information from the data itself instead of imposing an untested restriction in advance. It is against this backdrop that this study estimated the relationship between housing price and its associated characteristics using a flexible model of Box-Cox transformation framework without a priori restriction. With this framework, the importance of structural housing characteristics that were more pronounced than any other housing traits was revealed. Therefore, it is important for both public and private operators in the housing market to give preference to structural, neighbourhood and locational characteristics of housing since they have great influence on housing price.
In real estate valuation and house market research, house prices and rental value are generally analyzed by hedonic ,model ,based on micro ,economic theory. Hedonic model ,examines ,the effect of characteristics of ,goods ,on their prices. Factors that determine the house prices in Turkey are analyzed in this paper using 2004 Household Budget Survey Data. The most important variables that affect house rents are type of house, type of building, number of rooms, size, and other structural characteristics such as water system, pool, natural gas. Keywords: House price, Hedonic regression model, Turkey. ÖZET: Emlak değerlemede ve konut ,piyasası araştırmalarında konutun piyasa değeri genellikle mikro ekonomik teoriye dayanan hedonik model yoluyla analiz edilmektedir. Hedonik model, bir malın özelliklerinin fiyat üzerindeki etkisini incelemektedir. Bu çalışmada Türkiye’de konut fiyatlarını belirleyen faktörler 2004 HanehalkıBütçe Anketi kullanılarak analiz edilmiştir. Konut fiyatlarınıetkileyen en önemli değişkenler konutun tipi, yapı türü, oda sayısı konutun büyüklüğü ve diğer yapısal değişkenlerden konutun su sistemi, havuz, doğal gaza sahip olmasıdır. Anahtar kelimeler: Konut fiyatı, Hedonik regresyon model, Türkiye.
The elements of sales comparison for residential property depend on a package of inherent attributes that are valued by consumers. These attributes can be classified into the following categories: structural, physical, neighbourhood and environmental. A model that incorporates adjustments for floor area, age, views and amenities such as availability of recreational facilities is presented and discussed. A multiple regression analysis with transactions-based data, using weighted least square to determine the discrete estate-type induced price effect in the Hong Kong housing market, is also presented. The paper demonstrates how the view of negative housing attributes is capitalised into house prices. Specifically, it is shown that the residential property values are higher for estate-type housing properties, and lower for dwelling units with a cemetery view.
This paper attempts to estimate real estate prices from a Hong Kong perspective. A hedonic price model is estimated utilizing
housing transaction data during a period of relatively stable property price movements. Empirical results suggest that while
a residential property of larger size, higher floor level, and better view commands a higher transaction price, a property
lying closer to the mass transit railway station also commands a price premium. The nonlinear effect of floor level has also
been confirmed to exert an impact upon prices. Furthermore, an apartment located on an “unlucky number” floor is not preferred
by homebuyers and is usually transacted at a price discount.
Determinants of house prices in Turkey are examined in this paper using the 2004 Household Budget Survey Data. In property valuation and housing market research, the locational value is usually analyzed by hedonic methods that use multiple regression techniques on large data sets and require a formality based on microeconomic theory in the analyses. Because of potential non-linearity in the hedonic functions, artificial neural network (ANN) is employed in this study as an alternative method. By comparing the prediction performance between the hedonic regression and artificial neural network models, this study demonstrates that ANN can be a better alternative for prediction of the house prices in Turkey.
The objective of this study was to assess the influence of consumer behaviour on the variables determining residential property values in Lagos metropolis and how Nigerian valuers are incorporating these in their valuation exercise. The survey which was carried out within a period of three months (January, February and March 2006) involved buyers and valuers which participated in the sales of some properties within Lagos Metropolis. These buyers and valuers were asked to rank on a 4-point likert scale; the relative importance of each of the property attributes which affect the price ofthe property as perceived by them. The significance of each attribute was then assessed using Relative Importance Index (RII). The significant difference between the ranking of the buyers and valuers was assessed at each variable level and when all the variables were combined using the Kendall test of concordance and tested at .05 level of significant using chi- square approximation. Regressing the attributes of each property against the actual property paid, the significance of each attribute was determined; which was then compared with the ratings of both the valuers and the buyers. The study revealed that the methods of valuation used by the Nigerian estate surveyors and valuers do not take into consideration the buyers preferences in the assessments of residential property value.
Main variables influencing residential property values using the entropy method-The case of Auckland
- X J Ge
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A review paper on artificial neural networks: A prediction technique
- M S Mhatre
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