Vision-based housing price estimation using interior, exterior &amp; satellite images

Nouriani, Ali; Lemke, Lance

doi:10.1016/j.iswa.2022.200081

Cited by 14 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over the last years, a new domain has been added to the research on property valuation, which is extraction of useful visual features from image data using AI and computer vision to improve the predictive performance of real estate appraisal methods (Nouriani and Lemke, 2022). Visual features have been introduced as key factors in estimating the market value of the real estate assets (Elnagar and Thomas, 2020).…”

Section: Visual Featuresmentioning

confidence: 99%

“…Many authors have used them as additional factors to textual features for the development of different types of AVMs based on HPM, ANN, and different ML-based techniques. Some of them have also evaluated the efficacy of adding such visual features to other textual value-related factors and indicated the capabilities of visual features in increasing the accuracy of the AVMs (De Nadai and Lepri, 2018;Helbich et al, 2013;Kostic and Jevremovic, 2020;Law et al, 2019;Lee and Park, 2020;Liu et al, 2018;Muhr et al, 2017;Nouriani and Lemke, 2022;Poursaeed et al, 2018;Solovev and Pröllochs, 2021).…”

Section: Visual Featuresmentioning

confidence: 99%

See 1 more Smart Citation

A Framework to Integrate Bim With Artificial Intelligence and Machine Learning-Based Property Valuation Methods

Jafary

Shojaei²,

Rajabifard³

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Property valuation is of extreme importance since variations in the real estate market enormously influence people’s life. The main goal of Automated Valuation Models (AVMs) is to calculate the market value of a large number of properties with an acceptable accuracy. The Hedonic Price Model (HPM) is the most widely used AVM for the valuation purposes. Despite its simplicity, ease of use and straightforwardness, HPM lacks the capability to address the non-linear relationships between different value-related factors. Hence, researchers have developed other state-of-the-art property valuation methods based on the advancements in computer science including Artificial Intelligence (AI), Machine Learning (ML), computer vision and deep learning. Design, development, and validation of such advanced AVMs require establishment of a database including data on the different influential factors. Two types of factors are used in the literature, including textual and visual features. Reliable data sources are required for the implementation of AVMs since the accuracy of the provided valuations is definitely linked to the reliability of the used real estate databases. Building Information Modelling (BIM) provides precise information on different components of properties. Although some scholars have tried to use BIM for property valuation, BIM benefits in different valuation procedures have not been fully investigated. Hence, this paper provides a framework that consider BIM capabilities to be integrated with different stages and processes in property valuation, especially in relation to advanced AVMs based on AI and ML.

show abstract

Section: Visual Featuresmentioning

confidence: 99%

Section: Visual Featuresmentioning

confidence: 99%

A Framework to Integrate Bim With Artificial Intelligence and Machine Learning-Based Property Valuation Methods

Jafary

Shojaei²,

Rajabifard³

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…In order to determine the luxury level of the house, the KNN, SVM and Convolutional Neural Network (CNN) methods applied on the visual data were compared and it was observed that the CNN method gave the lowest Median Error Rate. It has been observed that the created model is more successful than Zillow's Zestimate method [24]. In another application stock price estimation was made, the decision tree model, multiple regression and random forest algorithms applied to five different stocks tried to predict the closing prices of the stocks the next day and the experimental results of these algorithms are successful [12].…”

Section: Introductionmentioning

confidence: 99%

Using Linear Regression For Used Car Price Prediction

MUTİ

Yildiz

2023

International Journal of Computational and Experimental Science and Engineering

View full text Add to dashboard Cite

Recently, there have been studies on the use of machine learning algorithms for price prediction in many different areas such as stock market, rent a house and used car sales. Studies give information about which algorithm is more successful in price prediction using different machine learning methods. The most commonly used method for price prediction is the linear regression model. In our study, we examined the effectiveness of the linear regression model for used car price prediction. In our study, we applied the linear regression model on a data set that includes the features and price information of vehicles in Turkey for the year 2020. As a result, when we selected 1/3 of the data set as the test data, we observed that the R2 score for the prediction success of our model was 73%. More successful results can be obtained with different data sets or a more detailed data preprocessing.

show abstract

“…Each study on the development of real estate valuation models accounts for some relevant internal and external aspects of property units such as structural, geometrical, locational, environmental, socio-economic and legal factors (French, 2004; Demetriou, 2018). In addition to these important factors, some scholars also use AI, computer vision and deep learning to extract some visual features from image data, including internal images, external images and remote sensing data, to enhance the predictive performance of the AVMs (Nouriani and Lemke, 2022).…”

Section: Introductionmentioning

confidence: 99%

BIM and real estate valuation: challenges, potentials and lessons for future directions

Jafary¹,

Shojaei

Rajabifard

et al. 2022

ECAM

View full text Add to dashboard Cite

PurposeBuilding information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.Design/methodology/approachThis paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.FindingsFour domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.Originality/valueThis paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

show abstract

Vision-based housing price estimation using interior, exterior & satellite images

Cited by 14 publications

References 13 publications

A Framework to Integrate Bim With Artificial Intelligence and Machine Learning-Based Property Valuation Methods

A Framework to Integrate Bim With Artificial Intelligence and Machine Learning-Based Property Valuation Methods

Using Linear Regression For Used Car Price Prediction

BIM and real estate valuation: challenges, potentials and lessons for future directions

Contact Info

Product

Resources

About