P-value of variables

P-value of variables

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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...

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... p-values of the room number and direction variables were determined as 0.0693 and 0.0622 (higher than 0.05) respectively. First, it is estimated that the number of rooms variable with the highest p-value (0,0693) should be excluded from the model (Table 2), and when the test is reapplied without this variable, the p-value of the façade variable has fallen below 0,05 (0,0309) ( Table 3). After the variable selection is complete, coefficients of the variables are determined by applying stepwise regression. ...

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... 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). ...
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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.
... 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). ...
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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.
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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.
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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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s 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.