(Ozkan et al. 2007) | Area of the residential real estate, age, floor condition, facade, basement area coefficient, floor area coefficient, region | Artificial Neural Network, Regression |
(Selim 2009) | Locational characteristic, type of house, age of building, type of building, saloon floor, living room floor, bathroom floor, heating system, number of rooms, size, sauna-jacuzzi, toilet, garbage grinder, water system, hot water, cable television, elevator, garage, pool, natural gas | Artificial Neural Networks, Hedonic Regression |
(Nas 2011) | Number of rooms, area, floor information, number of floors, facade, parking lot, age of the building, heating status, road condition, distance to transportation points, distance to education areas, distance to health centers, distance to police stations, distance to parks | Artificial Neural Network, Support Vector Machines |
(Yalpir et al. 2014) | The number of rooms, the number of stories, the storey of the residential real estate, age, frontier, distance to transportation network, distance to green areas, distance to trade centers, distance to university | Artificial Neural Network, Multiple Regression Analysis |
(Pow et al. 2014) | Living area, number of bedrooms, number of bathrooms | Linear Regression, Support Vector Regression, K Nearest Neighbors, Random Forest Regression |
(Borde et al. 2017) | Year/quarter, distance to airport, distance to Altamount road, distance to Vashi, distance to Virar, shopping mall count, hospital count | Linear Regression, K Nearest Neighbors, Random Forest |
(Ravikumar 2017) | Latitude, longitude, housing median age, total rooms, total bedrooms, population, households, median income, price, ocean proximity | Random Forest, Neural Network, Gradient Boosted, Bagging, Support Vector Machine, Multiple Regression |
(Abidoye and Chan 2018) | Number of bedrooms, number of bathrooms, property type, number of boys’ quarters, parking space, age of building, number of floors, availability of security fence, availability of sea view, location of property | Artificial Neural Network, Hedonic Pricing Model |
(Georgiadis 2018) | Age, condition, floor, luxury, no elevator, parking, size, view, distance to city’s main road, distance to a city's secondary road, distance to the ring road, distance to the coastal line, distance to the closest hospital, distance to the university campus, distance to the closest ex-military post, distance to the closest cemetery, distance to the industrial zone, distance to the closest sports arena, distance to the closest public square, distance to the closest park, distance to the city center, center, seafront, main road, secondary road, the ring road, near stadium, near park, near public square, near cemetery | Spatial Auto-regressive Models, Geographically Weighted Regression, Artificial Neural Networks |
(Ulvi and Ozkan 2019) | Age, number of floors, floor, facade, number of rooms, flat area, heating type, distance to social facility | Artificial Neural Network, Fuzzy Logic |
(Oshodi et al. 2019) | Bedroom, bathroom, parking, park, dining, lounges, balcony, kitchen, pool, floor area, furnished, services, garden, police | Artificial Neural Network |
(Deaconu et al. 2022) | Zone (distance from the city center), zone (neighborhoods), useful area, rooms, parking, bathrooms, balcony, finishing, partitioning, construction type, elevator, insulated windows, metal door, own central heating, storage room or attic, thermally insulated, cultural-social level, environmental pollution, urban density, business centers, farmers’ markets, financial and banking institutions, green area, health institutions, hotels, hypermarkets, religious institutions, relaxing places, schools, shopping centers, sport centers, transportation lines, universities | Artificial Neural Network, Generalized Linear Model |
This Study | Age of the building, floor, usage area, number of rooms, balcony, elevator, flat facilities, landscape, distance to sea, education areas, transportation centers, health areas, green areas, religious facilities, cemetery areas, shopping centers, and sports areas | Support Vector Machines, K Nearest Neighbors, Random Forest |