Selling Price Of House Research Articles

Machine Learning based Predicting House Prices using Regression Technique

Predicting the price of a house helps for ascertain the house's selling price in a specific area and assist individuals in determining the ideal moment to purchase a home. Our goal in this machine learning task on house price prediction is to use data to develop a machine learning model capable of predicting housing values in the specified area. We will implement a linear regression algorithm on our dataset. By using real world data entities, we are going to predict the price of the house in that area. For better results we require data pre-processing units to increase the model's efficiency for this project we are using supervised learning, which is a part of machine learning. We have to go through different attributes of the dataset. This project provides us an overview on how to predict house prices using various machine learning models with the help of different python libraries. This suggested model is thought to be the most accurate one for estimating home prices and makes the most accurate predictions. This offers a succinct overview, which is necessary in order to forecast the price of the home. This project consists of what and how the house price model works with the assistance of machine learning technique using scikit-learn and which datasets we will be using in our proposed model. Key Words: house price, lasso regression, ridge regression, R-squared.

Rancang Bangun Sistem Prediksi Nilai Jual Properti Berbasis Web Menggunakan Regresi Linear

This research was conducted to provide a clearer and more accurate depiction of the property value at Ziva Graha Property based on the physical characteristics and location of the houses. The study utilized PHP for data processing and modeling. The data employed included information on land area, building area, year of construction, number of floors, number of bedrooms, number of bathrooms, number of garages, and selling prices. This data was explored and processed using PHP software to assess the efficacy of linear regression methods in predicting house values. The research findings indicate that variables such as land area, building area, and number of bedrooms significantly influence the house's selling price at Ziva Graha Property. Other variables, such as the number of floors, number of bathrooms, distance to the city center, and road width, have a smaller impact. It is expected that the results of this research can provide a useful guide for prospective buyers and sellers in determining fair and realistic prices based on the physical characteristics of the houses.

The influence of sustainable features on liquidity of houses in the Czech Republic

The construction industry has a significant impact on the environment, and it especially affects the level of greenhouse gases. It also reduces the greenhouse gases through constructing and operating sustainable buildings. Sustainable buildings include buildings that meet basic sustainable principles, namely environmental, social, and economic. Sustainable buildings are very often associated with certified buildings (LEED, BREEAM). However, sustainable buildings do not have to be certified to fulfill the idea of sustainability. The question is, how much does sustainability affect the selling prices of houses. A database of 570 sold houses in the district of Brno – venkov in the period of years 2017 - 2019 was compiled. It was found that 5.6% of the houses featured some sustainable aspects, such as heat recovery systems, natural construction materials and all alternative technologies. The factors of liquidity were considered and evaluated. In case of the sustainable houses 3.0 % higher ΔP (difference between asking and market price) was detected. Influence on time on the market was not proven. Based on ANOVA analysis, the impact of sustainable features of houses on the selling prices was not found. Analysis was carried out by a semi-database of 122 houses sold in the year 2017.

Home Purchase Decision Support System Using the Brown-Gibson Method

thesis. The application program was built by applying the Brown-Gibson method which is capable of measuring both qualitative and In making a decision to buy a house, a buyer will consider a variety of complex criteria, such as qualitative (non-numeric) and quantitative (numeric) criteria to get the best decision. To simplify the process, a home purchase decision-making application program was created in this quantitative criteria. The algorithms in the Brown-Gibson method used in this application program include: "Performance Measurement" to measure quantitative criteria, such as house selling price, land area, building area and number of bedrooms; “Pairwise Comparison” to measure qualitative criteria, such as architectural style, position of the building, proximity to the work environment, proximity to shopping centers, proximity to the educational environment, state of the home environment, building status, house certificate; and "Preference of Measurement" which combines the quantitative and qualitative criteria to produce a priority weight that can be accepted as a measurement result for each alternative house as a whole. The final result of this thesis is an application program that provides the highest value for an alternative home. The house that gets the highest score is an alternative house that has the most suitable specifications according to the criteria considered by the buyer. The house that gets the highest score is an alternative house that has the most suitable specifications according to the criteria considered by the buyer. the condition of the house environment, the status of the building, the certificate of the house; and "Preference of Measurement" which combines the quantitative and qualitative criteria to produce a priority weight that can be accepted as a measurement result for each alternative house as a whole. The final result of this thesis is an application program that provides the highest value for an alternative home. The house that gets the highest score is an alternative house that has the most suitable specifications according to the criteria considered by the buyer. and "Preference of Measurement" which combines the quantitative and qualitative criteria to produce a priority weight that can be accepted as a measurement result for each alternative house as a whole.

House Price Forecasting Based on Hybrid Multi-regression Model

It is important to manage the production by analyzing the demand in the market. The market faces uncertain demands, short life cycle and lack of historical sales data due to which, forecasting becomes challenging. Various approaches have been proposed over the past few decades concerning this issue. This paper forms a basis for understanding the prediction mechanism by presenting a comprehensive literature review along with various domains in which sales forecasting can be done. For any prediction process we can’t define a certain model that will just outperform every other model but we can try and find suitable model according to the data. We have defined various models and also a hybrid model to predict the selling price of house based on its features, thus feature engineering is also applied to extract a fruitful dataset.

Seller - Paid Concessions from 2004 to 2012: Implications for House Selling Price and Days on the Market

In this paper, we examine the extent to which seller-paid concessions (SPCs) are capitalized into foreclosed and non- foreclosed housing selling prices and days on the market (DOM) from 2004:Q1–2012:Q1. The findings suggest that SPCs are fully capitalized into the net selling price of non-foreclosed properties but a 6.7%–15.0% price premium occurs for foreclosed property prices. Non-foreclosed transactions with SPCs have up to 19.5% longer DOM during this time period. The effect of SPCs on price and DOM vary before and after the housing debacle, supporting other studies finding that economic conditions influence price premiums and discounts through changes in underlying explanatory variables.

OPTIMASI FUZZY INFERENCE SYSTEM SUGENO DENGAN ALGORITMA HILL CLIMBING UNTUK PENENTUAN HARGA JUAL RUMAH

A house selling price can be determined by two methods, financially and technically. Hovewer, the selling price that determined by the methods are often different. It makes the manager having a problem when determining the house final selling price accurately. This paper proposes Sugeno Fuzzy Inference System (FIS) to calculate an accurate price. To get better result, Hill Climbing Algorithms is used to optimize the membership function of Sugeno FIS. A series of computational experimens prove that the approach is effective. Hill Climbing Algorithms can improve the accuracy of results.

The Effect of Relisting on House Selling Price

When house sellers reach the end of a listing contract without a sale they are faced with several decisions. A seller who wants to continue to market the property can leave it on the market, relist the property immediately, or take it off the market for a period of time before relisting it. Research has shown that properties with longer time-on-market may carry a stigma and sell for less. In an attempt to mitigate the negative perception of a house that other buyers appear to have passed by, a seller can have the agent relist the property so it appears as a new listing. If a seller decides to relist the property, the owner also has to decide how long to wait before relisting. We use a hedonic approach to investigate the choices sellers have when deciding whether to relist their property and the impact those decisions have on the property’s selling price. We find that relisting a property results in a higher selling price and that to maximize price, the seller should relist the property with the same agent within 30 days.

House selling price assessment using two different adaptive neuro-fuzzy techniques

Abstract It is of high importance to estimate the house selling prices because sellers and buyers need this information in an accurate way at time of sale. As a result of the fact that the estimation of the house prices is extorsive considerable research has been published in the literature on estimating house prices. This study covers the application of two different adaptive neuro-fuzzy (ANFIS) approaches for the estimation of house selling price. In this study, two different ANFIS models, namely ANFIS with grid partition (ANFIS-GP) and ANFIS with sub clustering (ANFIS-SC), were used in forecasting house prices, and the results were evaluated. Comparison of results from the two techniques indicated that the ANFIS-GP models performed better than the ANFIS-SC models. The study suggests that the ANFIS-GP technique can be successfully used in the estimation of house prices in the construction sector.

The use of fuzzy logic in predicting house selling price

In this paper, a new grading model has been developed for prediction of the selling price of house-building. Fuzzy logic systems, considering the city plans, the nearness to cultural, medical, training and educational buildings, the public transportations systems, the other environmental factors and the increased technological upgrading deals with information about construction, have been employed in order to construct the model and achieve the aim. Such factors are used as the inputs. Besides, a questionnaire application including these factors has been applied to determine the values of fuzzy training and testing sets. In this way, the constructed model has been applied to the prediction of selling prices of houses located in different regions of Eskişehir city in Turkey. The predicted values and real selling prices determined by selling market have been compared with each other. Consequently, real selling price of house-building has shown variety with respect to the regional aspects and salesmen. The suggested fuzzy logic model can be capable and usable for similar applications.

Predicting a house's selling price through inflating its previous selling price

This paper considers how accurately inflating the previous selling price of a modern property predicts its selling price. Predicting a house's value is an important capability as it allows how the asking price affects the time to sale and the price achieved, to be modelled. The analysis is carried out on a data set of 105 pairs of earlier and later selling prices for UK properties constructed since January 1999. As an alternative to using published house price indices for inflating the prices, a novel approach for modifying the published house price indices through the use of observed repeat sales of properties is put forward and analysed. Using the best published index gives an average predictive error of 10.9% while using the published index modified by repeat-sales information, gives an average predictive error of 8.4%.

Repair Expenses, Selling Contracts and House Prices

This article examines the impact of repair expenses on the selling price of a house. Using data that include the actual dollar amount of repairs stipulated in the settlement statement, we investigate the frequency and extent to which the performance of major repairs is part of the sales contract. We find that most homes are restored to a “normally maintained” state each time the home changes hands, and that the cost of bringing the home to this condition is included as part of the house selling price. This implies that it may be unnecessary to measure maintenance levels when using transaction data to study components of house price or to construct house price indexes.

The effect of maintenance expenditures on the measurement of depreciation in housing

This paper has two objectives: (i) to construct a model of durable good depreciation applicable to housing, inclusive of maintenance effects; and (ii) to indicate problems of misspecification if maintenance is ignored. The depreciation rate for housing has been estimated by examining price data on units of various ages.’ The rate of change of observed house prices with respect to age is interpreted as a depreciation rate. When this is applied to actual data, paradoxical results have been obtained. Negative estimates of depreciation rates for rental apartment units arise in Gillingham (1975), and for nonresidential structures in Hulten and Wykoff (1977). Negative results are possible because the “log price on age” regression measures net depreciation, after maintenance and addition. Gross depreciation, which cannot be negative, is the sum of net depreciation and maintenance. If more repair expenditure is allocated to a house, it will depreciate less than one of similar quality but unrepaired. This paper incorporates an explicit treatment of expenditure on repair. Homeowners have control over this expenditure, the product of an implicit price of repair services and an efficiency index of capital added. In Section 2, the basic model is derived, which constructs the expected current selling price of a house as the discounted present value of housing services consumed less those added by maintenance. This house selling price, under certain assumptions, is expressible as the product of a hedonic price index