Table 1
Source publication
In the case study, estimation, contributions and related issues of the hedonic valuation model in housing markets were analyzed by means of hedonic models developed for the Cukurambar Region in Cankaya District of Ankara Province. The marginal effect of housing characteristics on house prices were examined by the hedonic valuation model, and struct...
Context in source publication
Context 1
... β1>0, β2>0, β3>0, β4>0, β5>0, β6<0, β7>0, β8>0, β9>0 It is estimated that among the parameters used, only higher building age will negatively affect the value, and the β value of building age in the model is determined to be less than zero. After the variable selection was completed, the Constrained Linear Least-Squares method was applied and the coefficients after the regression were calculated (Table 1). ...
Citations
... The price data used in the studies were obtained from sale advertisements posted on the internet (Hayrullahoğlu et al., 2018;Yılmazel et al., 2018;Aydemir et al., 2020;Alkan et al., 2022;Doğan et al., 2022), from the Central Bank of the Republic of Türkiye (Akay et al., 2019), and from valuation reports prepared by valuation experts that hold a license from Capital Markets Board (CMB) (Saraç, 2012;Esen & Tokgöz, 2021). The house values (actual sales prices) used in this study were gathered from real estate agents, CMB-licensed real estate valuation experts, home builders, and field studies that included face-to-face interviews with buyers/sellers. ...
... Although studies on the house-price relationship were conducted in different locations and using various methods, the area, age, and floor parameters were always found to have a determinant effect on price in the majority of studies (Yu et al., 2007;Sevgen & Tanrivermis, 2020;Yazdani, 2021). Even though spatial data boosted the adjusted R 2 value of the model, Hayrullahoğlu et al. (2018) found that structural factors were more beneficial than spatial and environmental characteristics. ...
Residential real estate is regarded as a safe and profitable investment tool while also meeting the basic human right to housing. The fact that there exists a large number of parameters both affecting the value of a house and varying based on place, person, and time makes the valuation process difficult. In this regard, accurate and realistic price prediction is critical for all stakeholders, particularly purchasers. Machine learning algorithms as an alternative to classical mathematical modeling methods offer great prospects for boosting the efficacy and success rate of price estimating models. Therefore, the purpose of this study is to investigate the applicability and prediction performance of the tree-based ML algorithms -Random Forest (RF), Gradient Boosting Machine (GBM), AdaBoost, and Extreme Gradient Boosting (XGBoost)- in house valuation for Artvin City Center. As a result of the study, the XGBoost and RF algorithms performed the best in estimating house value (0.705 and 0.701, respectively) as determined by the Correlation Coefficients (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) metrics. Thus, it can be said that ML algorithms, particularly XGBoost and RF, perform satisfactorily in residential real estate appraisal even with modest amounts of data and that the success rate grows as the amount of data increases.
... This study uses Root Mean Square Error (RMSE) [25], Coefficient of Determination (R2), and Mean Absolute Percentage Error (MAPE) to measure model performance. The smallest RMSE value will indicate the best model and is defined in (9). R2 is used to assess the effect of the independent variables used in the best model on the "price" variable and is mathematically shown in (10). ...
The transition of BPHTB management from central taxes to regional taxes is a continuation of the regional autonomy policy. The difference between the market value and the prevailing NJOP poses a challenge for the Sintang District Government in determining the Tax Object Acquisition Value (NPOP) as the basis for imposing BPHTB. Machine learning has been extensively explored for predictions and can be an alternative that can help predict NPOP, especially house prices. This study uses backward elimination and forward selection methods to select the features used in this study and multiple linear regression and K-Nearest Neighbor methods to make house price prediction models. The results of model performance measurement using RMSE, Multiple Linear Regression method with feature selection using backward elimination resulted in a better model with an RMSE value of 44.02 (million rupiahs) and an R2 value of 0.707.
... Housing basically meets the need for accommodation; however, it can be seen as an investment and financing tool as the economic status and life expectancy of people change (Hayrullaho glu et al., 2018). In this respect, there is an important distinction between a needoriented housing unit with minimum standards and the housing unit demanded by solvents. ...
Purpose- This study aims to explore the housing demand of urban fringe residents in southwest Ankara. Two subquestions were developed: What are the respondents' perceptions of Ankara city center and which characteristics do they prioritize for living in the urban fringe? Data were collected through a face-to-face household survey, and a hedonic regression model was developed based on responses.
Design/methodology/approach- Increasing housing demand, lifestyle change and faulty housing policies in Ankara have triggered urban sprawl along fringe areas, which causes several urban problems. Considering that urban sprawl is related to housing demand, it is essential to examine the structure of housing demand and the preference to live near the urban fringe.
Findings- According to the survey results, security, crime, noise pollution, traffic congestion and parking problems that reduce the welfare of Ankara city center encouraged expansion toward the rural-urban fringe, in addition to low-quality or traditional housing attributes. The urban core became unattractive to the respondents for being insecure, chaotic and down-market. The hedonic model showed that seven variables, all related to housing characteristics, best explain the housing demand in the area. Socioeconomic status and lifestyle were found to be associated with the desire to live on the urban fringe, also indicating the snob effect.
Originality/value- The authors propose taking domain-specific housing demand patterns in the spatial planning assumptions and housing policies into consideration for a well-governed urban development in Ankara. Making the city center more appealing through rehabilitation should be preferable rather than limiting demand on the urban fringe with a strict intervention in housing supply.
... These models focused on the size and soil characteristics of the land as these relate to the total surface area that can be used for crop production. Hence, the variable considered as salient is the rent of the land, as pertains in one breath and the future economic value of the land as anticipated by the rational agents in the long run (Hayrullahoglu et al. 2018). This is underpinned by the general assumption that there are proximate variables with varying characteristics that would influence the hedonic pricing of the land in a location (Reydon et al. 2014). ...
Land-use policies meant to mitigate deforestation activities in Ghana will have to consider the heterogeneity of the drivers of arable and forest land degradation. This would help avoid the one-size fits all approach to solving this problem. The urgency for this realisation is premised on the recent increasing monetary incentive to convert arable and forest land to other land uses in peri-urban Ghana. This study hypothesised that there is no significant relationship between land rent and the conversion probability from arable and forest land to other land uses such as commercial, industrial and residential land uses in Bosomtwe, a peri-urban district in the Ashanti region of Ghana. Four-hundred and one usufruct or land-owning households and individual landowners participated through a three-stage sampling procedure. The results indicated a significant relationship between higher land rent and conversion probability from arable land to other land uses such as commercial, industrial and residential land uses. Specifically, receiving land rent above GH¢400 (OR = 1.979) predicted the outcome variable in all three models. Moreover, being a female (OR = 0.612), ageing: 56 and 65 (OR = 2.158) and 76 and above (OR = 11.781), traders/food vendors (OR = 0.423) and widows (OR = 2.050) had some odds of predicting the outcome variable. The study recommends a reformation of government land use conversion policies and decisions in collaboration with landowners, to include parameters which assess the effect and benefits of land conversion decisions on biodiversity before leasing out land rights.
... It finds a linear regression model by analysing the x and y variables. The PLS model is also known as bilinear factor model designed to deal with multiple regressions when data has a small sample, multicollinearity or missing value (Gizem and Yesim 2018). This model is also being implemented in case studies that are related to price prediction for apartment and real estate investment (Wezel and Potharst 2005b; . ...
... This model is also being implemented in case studies that are related to price prediction for apartment and real estate investment (Wezel and Potharst 2005b; . This model was very popular in hard science such as chemometrics and chemistry which deal with a big problem with high number of correlated variable and a limited number of observations (Gizem and Yesim 2018). ...
This volume showcases selected conference papers addressing the sustainable future of ASEAN from the perspectives of business and social science disciplines. In addressing the 17 Sustainable Developments Goals (SDGs) envisioned by the United Nations in the domains of environment, health and well-being, posing potential means of reducing inequalities globally, the authors target specific issues and challenges confronting the fast-growing region of ASEAN and present suggestions for co-operation and commitment from governments, non-governmental organisations (NGOs) and society at large, in line with the ASEAN Vision 2020. Papers are selected from the 3rd International Conference on the Future of ASEAN (ICoFA) 2019, organised by Universiti Teknologi MARA in Malaysia, whose conference theme “Charting the Sustainable Future of ASEAN” enables intellectual discourse on sustainability issues from business and the social sciences, as well as science and technology. The selection of papers is published in two volumes, comprising scholarly and practical insights into sustainability in ASEAN. This first volume of papers from business and social science scholars will be of interest to researchers and policymakers interested in sustainability developments in the ASEAN region.
... Hedonic models, which are basically regression equations, are estimated with the help of regression analysis. The model is based on the assumption that goods are heterogeneous, and each property is described as the sum of its individual properties (Hayrullahoğlu et al., 2018). At the forefront of addressing the need for shelter in urban spaces, which has been shaped in our days through large population movements, houses have changed form in line with social behavior, economic status and demands of individuals over time, have started to carry different qualitative and quantitative features, and even regarded as important investment and financing tools. ...
... Just like any other heterogeneous property, houses also contain more than one feature, and are sold as a collection of the features they have. Since it is very difficult to specify the price of goods with multiple features at a single total price and to analyze the market, the price of the goods is identified by determining the price of each feature of the good, and it is called hedonic pricing (Hayrullahoğlu et al., 2018;Hülagü, Kızılkaya, Özbekler, & Tunar, 2016;Selim, 2008;Hidano, 2002). ...
Housing composite commodity as a one of the most important property of households, has been interesting for researchers and planners in last years. Hedonic pricing, estimating the value of housing characteristics through the use of transactions data. For this purpose, the main aim of this paper is to evaluate the factors affect housing prices in the Niyavaran region of Tehran, Iran. Nowadays,
the housing planning tries to meet the needs of users, which this subject is one of the important factors in sustainable housing scheduling. To achieve this objective, first of all the variables influence the housing price have been determined and then, data was collected via the survey and was analyzed, using the Hedonic price model. The empirical results of this paper showed that variables: the building age, number of rooms, house view side, Interior decoration, lighting and distance to school are significant and positive and Parking, education and road traffic were significant and negative in this study. By applying these results in urban projects eventually can lead to the better life quality and sustainability in urban life.
Keywords: Hedonic price model, Housing, Physical variables, Environmental variables, Tehran.
Kamu konutlarının fiyatlarını belirleyen faktörlerin analiz edildiği bu çalışmada, konut fiyatlarının yapısal, konumsal ve komşuluk özelliklerince belirlendiği varsayımına dayanan hedonik fiyat modelinden yararlanılmıştır. Çalışmanın model uygulamasında, Ankara İli Çankaya İlçesinde bulunan ve Aralık 2019-Ekim 2020 tarihleri arasında ihale yoluyla satışı gerçekleşen Hazine mülkiyetindeki kamu konutlarına ilişkin yatay kesit verisi kullanılmıştır. Tahminler en küçük kareler yöntemi ile yapılmıştır. Modeller oluşturulurken bağımsız değişkenleri belirlemek için adım adım regresyon yönetimine başvurulmuştur. Elde edilen sonuçlara göre konutun bulunduğu parselin yüzölçümünün artması, konut büyüklüğünün artması, konutun ara katta bulunması, konutun bulunduğu binanın sokak(lar) ve/veya cadde(ler) kesişiminde konumlanması, konutun yaşının artması, konutun 500 metre yakınında metro durağının bulunması, konutun otoparkının bulunması, konutun bulunduğu mahalledeki park sayısının artması, konutun bulunduğu katın artması, konutun Bahçelievler, Emek, Yukarı Bahçelievler, Söğütözü, Konutkent, Birlik, 100. Yıl, Bayraktar, Maltepe, Harbiye, Yücetepe, Esatoğlu ve Fakülteler Mahallelerinde bulunması konut fiyatını pozitif yönde etkilemektedir. Konutun bodrum katta veya zemin katta bulunması, konutun bulunduğu binadaki toplam kat sayısının artması, konutun Kızılay şehir merkezine, alışveriş merkezine ve parka olan uzaklığının artması, konutun bulunduğu binanın sokak üzerinde konumlanması, konutun Güvenevler, Keklikpınarı, Aydınlar, Şehit Cevdet Özdemir ve Aşağı Öveçler Mahallelerinde bulunması konut fiyatını negatif yönde etkilemektedir. Bu çalışmanın özgün yanı kamu konutları üzerine yapılmış ilk hedonik fiyat modeli uygulaması olması ve gerçek satış fiyatlarının kullanılmasıdır. Elde edilen bulgular üç açıdan önemlidir; birincisi, çalışmada tamamı aynı ilçe sınırları içerisinde bulunan benzer özelliklere sahip konutlara ilişkin oldukça homojen bir veri seti kullanılmıştır. İkincisi, belli bir sosyoekonomik kesimin talebi incelenmiştir. Üçüncüsü, öncelikli alım hakkı sahipliği gibi belirli bir kesim lehine yasayla düzenlenmiş bazı satın alma kolaylıklarının ihalelerde rekabet şartlarını olumsuz yönde etkileyebileceğine dair elde edilen ampirik bulgulardır.
Bir tür kentsel gelişim morfolojisini betimleyen kentsel yayılma, dünya kentlerinin sürdürülebilirliği ile ilgili endişe verici sorun alanlarından biri olup, Türkiye’deki büyükşehirlerin de bu sorunla yüzleştiği görülmektedir. Politika yapıcıların farkında olmadan yayılmayı teşvik eden düzenlemeler yapması kentin çeperlerinde yeni yaşama mekânlarının oluşma eğilimini artırmakta ve düzensiz kentsel büyümeye yol açmaktadır. Kontrollü kentsel büyüme, doğal kaynakların yanı sıra finansal kaynakların da en etkin kullanımını vurgulamakta olup, kentlerin denetimsiz genişlemesi yerel yönetimlerce sağlanan hizmetlerin mali yükünü artırmaktadır. Kentsel yayılmanın çeperlerde yer alan bölgelerde yeni yaşam alanları kurulması ile ilişkili olduğu varsayıldığında, çeperlerdeki konut arz ve talep göstergelerini kontrollü kentsel büyümeyi başarmak için göz önünde bulundurulması gereken faktörler arasında göstermek mümkündür. Bu nitel araştırmada kentsel yayılma alanları ve bu alanlardaki konut talebine etki eden sosyoekonomik ve mekânsal faktörler ele alınmıştır. Dünyadaki eğilimler, kontrollü bir kentsel büyüme için politika yapıcıların müdahaleci olmak yerine yönlendirici bir rol üstlenmeleri gerektiğini ortaya koymaktadır. Bu bağlamda, kentsel büyüme eğilimini ölçmek ve kontrolsüz büyümeye daha etkin müdahale edebilmek için kentsel yayılma alanlarında kent merkezinden farklılaşan konut dinamiklerinin ve bu alanlardaki konut talebine ilişin varsayımların dikkate alınması gerektiği sonucuna varılmaktadır.
Housing price prediction in real estate industry is a very difficult task, and it has piqued the interest of many researchers over the past years in the quest to look for a suitable model to predict the price of property. For this reason, this paper aims to review the literature on the application of modelling technique that is usually being implemented to indicate the price prediction for properties. The modelling technique includes the Artificial neural network (ANN), Hedonic price model (HPM), Fuzzy logic system (FLS), Support vector machine (SVM), Linear regression (LR), Decision tree (DT), Random forest (RF), K-nearest neighbour (KNN), Partial least square (PLS), Naïve bayes (NB), Multiple regression analysis (MRA), Spatial analysis (SA), Gradient boosting (GB), Ridge regression, Lasso regression and Ensemble learning model (ELM). All these models were reviewed, and the explanation of advantages and disadvantages of each model was included. Hence, this paper reports the findings of reviews made on models which deal with regression and classification problem.
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The arts and science in determining the residential property value make property value has evolved due to the changing in external factors such as economy, environmental and social. This research is aim to determine the residential property value by taking into account the economic attributes that could affect the value of residential property in flood risk areas. The economic attributes consist of structural, locational and environmental attributes involved in residential property valuation in relations with flooding. This paper will discover the significance and effect of each economic attributes in determining residential property value in flood risk area. An extensive review of previous studies in economic valuation of property for different floods disaster studies. It is considered to be the main restrictive factor resulting in lack of empirical studies in this field. Practitioners and researchers will find this study useful in developing an improved understanding of the economic valuation of flooding. The finding reveals that the economic attributes response to floods for a residential property value with positive, negative and none expected effects.
