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Analysis of the Main Determinants of Price in Housing Buildings in Lisbon

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Analysis of the Main Determinants of Price in Housing Buildings in Lisbon Analysis of the Main Determinants of Price in Housing Buildings in Lisbon Parishes of Arroios, Beato, Marvila, Olivais, Penha de França, Santa Clara and São Vicente Carolina Pais Correia [email protected] Instituto Superior Técnico May 2019 Abstract The purpose of this research is to disclose the determinants of the sale price of residential properties in the city of Lisbon, particularly in the parishes of Arroios, Beato, Marvila, Olivais, Penha de França, Santa Clara and São Vicente. This work was based on a database with information on real estate sold in the municipality of Lisbon from 2008 to 2017, with a sample of 1986 properties sold in the parishes under study. For each parish, two models of property price prediction were developed using two statistical tools: multiple linear regression model and generalized linear model. The most recurrent predictive variables were the year of construction and the state of conservation of the property, and these variables tend to have a high weight. The quarter, the existence of an extra floor in the property and the existence of charges are not predictive variables of any of the parishes. The area, which normally has a great weight in the definition of real estate prices, is predictive on only three parishes and presents a very low weight. This is because the independent variable, the price of real estate, is given in the form of price per square meter, removing the largest contribution of the area on the property value. Comparing the two models of each parish, it is observed that the generalized linear model always has a better fit. The parish whose model has the best adjustment is Beato, and the one with the worst adjustment is São Vicente. Keywords: Housing market, price determinants, hedonic model different perspectives. In this work the focus 1. Introduction is directed to the price formation, exploring The price of residential real estate is the predictive models of real estate prices. currently of great importance for various The hedonic price method [1], allows sectors of society, namely governments, modelling the price of a product based on construction companies, real estate agents quantitative attributes associated with it, and the population in general. This is due to typically through a traditional linear the fact that the real estate market and the regression. This model was later applied to changes in sales values could affect the the real estate market [2], with the growth of the economy, the inflation and the integration of physical, environmental and banking sector, as well as social equity and accessibility characteristics to econometric accessibility. Given its importance and its models that sought to justify differences in instability, the housing market has been real estate values. strongly studied in recent years through 1 Over the last decades the hedonic price To better understand the expected model has been studied resorting to different behaviour of variables and which are the analysis tools and with different objectives, most used statistic tools, a few studies that which resulted in the use of a wide range of were carried out in similar scopes to this explanatory variables. Hedonic models have work were analysed. Table 1 marks the been used to model housing markets and variables and he statistic tools used in each housing price indices [3] [4] and identification of the studies. For each variable used, it’s of housing submarkets [5]. There were also signal and its magnitude in the model are authors that opted for other approaches, symbolised between parenthesis. The focusing their studies on the effect of certain magnitude is symbolised by how many times determinants of the property price, such as the signal repeats itself ([+][ - ]: low weight; proximity to railway stations [6], the theft [+++][- - -]: high weight). index in residential areas [7], or the distance to the city centre [8]. ____________________________________________________________________________ Table 1 - Variables (signal and magnitude) and Statistic Tools observed in other studies and Age Area Floor ance to the ance Garage State of Typology Reference City Centre Accessibility Conservation Statistic Tools Storage Room Transportation No. Bathrooms Dist Air Conditioning x x x [9] x (+) x (+++) x (+) x (+) x (+) x (-) QR (++) (++) (++) x x LR, [10] x (+++) x (-) x (+) (++) (++) GWR [11] x (--) x (+++) x (--) SLR [7] x (--) x (+++) x (+) LR, RR x [12] x (+) x (+) x (-) MR (++) [13] x (--) GWR [14] x (---) x (+++) x (---) LR QR - Quantile Regression; LR - Linear Regression; GWR - Geographically Weighted Regression; SLR - Semi-Log Regression; RR - Robust Regression (control of outliers); MR - Multiple Regression ____________________________________________________________________________ The most used explanatory variable to determinants underlying real estate prices estimate housing prices is the area, and it is depend on the particular context, which may also the one that generally presents greater vary in time and space. weight in the final sale price of the property. This weight is positive, which means that the The methodologies based on simple and price of the property tends to increase with multiple linear regressions are the ones with the increase of its area. The age of the the greatest presence in the literature, but in property is also of relevant in the definition of recent years there has been a trend towards the price of a property, so it is repeated in the exploration of increasingly complex many research efforts. The impact of this simulation models of the behaviour of the variable tends to have a negative sign, since real estate market. In the older studies, real estate deteriorates as their age linear regression was used because it was increases. The signal of each variable may the tool available. Still, even with the present some variation, but its strength is development of new modelling tools, in even more inconstant. The variable might particular artificial intelligence tools such as have a high impact in some studies and be artificial neural networks, linear regressions less relevant in others. An analysis of are used to establish a baseline to models used in several studies concluded benchmark the performance of the models that studies often disagreed about the sign developed with the more sophisticated tools and magnitude of the characteristic’s weight [16]; [17]. Another evolution in terms of in the final price [15]. This reveals that the modelling tools, is the use of approaches 2 that allow the incorporation spatial referring to the study areas are used, information related to the environment in completing a total of 1986 properties. The which the property is inserted into the database that supports this study presents hedonic models. With the increase on the information regarding the price of the use of GIS platforms to integrate spatial with properties and their characteristics. These non-spatial information, tools such as the characteristics constitute a set of variables to Geographically Weighted Regression be incorporated in the models, being these (GWR), or the Spatial Autocorrelation Model variables the following: i) Semester of Sale (SAC), among others, have been used to (SEM); ii) Quarter of Sale (TRI); iii) Parish address real estate prices. (FRE); iv) Construction Year (ACON); v) Building Floors (PEDIF); vi) Extra Building Floors (PEDIF_extra); vii) Typology (TIP); viii) Floor (PISO); ix) Extra Floor 2. Case Study and Methodology (PISO_extra); x) Area (CEDIF); xi), State of Conservation of Property (CIM); xii) The parishes of Olivais, Beato, Marvila and Abandoned (ABAND); xiii) Onus (ONUS); Santa Clara considered in the present works and xiv) Buyer (COMP). Extra building floors are classified by the real estate market in reflects the case of buildings with semi-attic Lisbon as the peripheral zones. These are in the last floor. The Extra floor reflects the the zones that are in the eastern periphery cases of duplex or triplex configuration of the of Lisbon (excluding the parish of Parque apartments. The independent variable das Nações), being further away from the (PVNA) is the selling price of each property centre of the city. These are areas marked reduced to the area unit and normalized to by an industrial past, of which there are still the average price of the year in which the several vestiges, and with some presence of transaction occurred. By doing so, the social housing. Despite being characterized influence of time on the housing prices is by aged and degraded buildings, a great removed. development is expected in the coming years, through the appearance of new The methodological approach of this work is leisure spaces, construction of new to test different models that simulate the enterprises and reformulation of behaviour underlying the formulation of the thoroughfares. These areas have an price of a property based on its extremely privileged location in terms of characteristics. For this, two tools were views to the Tejo River. tested: i) Linear Regression; and ii) Generalized Linear Modelling, with this The parishes of Arroios, Penha de França second to integrate the interaction of and São Vicente are designated as other variables and non-linearity. Firstly, studies historical zones. They are located between were done on the descriptive statistics of the the peripheral areas and the historic city database and the correlation matrix of the centre of the city, very attractive also for their variables under study. Then, through a first proximity to the business centre of Lisbon. linear regression, we obtained the Being older zones possess more historical statistically significant predictive variables patrimony than the peripheral zones, reason for each parish and, with the improvement of why they have more points of interest. the categories of the variables and the Closer to the city centre, they benefit from models and with the removal of the outliers better access and a greater offer of means identified, a linear regression model was of transport. Unlike the outlying areas, they obtained for each parish.
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