Housing Price Prediction Based on CNN

Piao, Yong; Chen, Ansheng; Zhen-dong, Shang

doi:10.1109/icist.2019.8836731

Cited by 37 publications

(23 citation statements)

References 4 publications

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“…Adopting secondary data for statistical analysis using ANN is not unusual in literature, an example is the adoption of datasets from propertyGuru websites (Ke & Wang, 2016); ingantlan 3 (Kutasi & Badics, 2016); and sahibiden 4 (Kitapci et al, 2017). Others include Kaggle (Phan, 2019); stats 5 (Piao et al, 2019), while Hu et al (2019) retrieved and adopted datasets from five foremost Chinese real estate secondary data sources 6 . The adopted dataset 255 was fed into the proposed model, which is divided into two distinctive parts to include the ANN and ANFIS.…”

Section: Related Researchesmentioning

confidence: 99%

An Integrated Approach Based on Artificial Intelligence Using Anfis and Ann for Multiple Criteria Real Estate Price Prediction

Yakub

Ali

Achu

et al. 2021

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A relatively high level of precision is required in real estate valuation for investment purposes. Such estimates of value which is carried out by real estate professionals are relied upon by the end-users of such financial information having paid a certain fee for consultation hence leaving little room for errors. However, valuation reports are often criticised for their inability to be replicated by two or more valuers. Hence, stirring to a keen interest within the academic cycle leading to the need for exploring avenues to improve the price prediction ability of the professional valuer. This study, therefore, focuses on overcoming these challenges by introducing an integrated approach that combines ANFIS with ANN termed ANFIS-AN, thereby having a reiteration in terms of ANN application to fortify price predictability. Using 255 property data alongside 12 variables, the ANFIS-AN model was adopted and its outcome was compared with that of ANN. Finally, the results were subjected to 3 different error testing models using the same training and learning benchmarks. The proposed model’s RMSE gave 1.413169, while that of ANN showed 9.942206. Similarly, using MAPE, ANN recorded 0.256438 while ANFIS-AN had 0.208324. Hence, ANFIS-AN’s performance is laudable, thus a better tool over ANN.

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Section: Related Researchesmentioning

confidence: 99%

An Integrated Approach Based on Artificial Intelligence Using Anfis and Ann for Multiple Criteria Real Estate Price Prediction

Yakub

Ali

Achu

et al. 2021

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“…Deep regression is the use of neural networks for regression tasks and it is a wide field of study with many different architectures and applications [14]. Some example applications of deep regression include housing price prediction from house images [22], television show popularity prediction based on text [12], image orientation and rotation estimation [5,18], estimation of wave velocity through rocks [9], stock prices [24,16,20], and age prediction [21,32].…”

Section: Deep Regressionmentioning

confidence: 99%

Using Robust Regression to Find Font Usage Trends

Tsuji¹,

Uchida²,

Iwana³

2021

Preprint

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Fonts have had trends throughout their history, not only in when they were invented but also in their usage and popularity. In this paper, we attempt to specifically find the trends in font usage using robust regression on a large collection of text images. We utilize movie posters as the source of fonts for this task because movie posters can represent time periods by using their release date. In addition, movie posters are documents that are carefully designed and represent a wide range of fonts. To understand the relationship between the fonts of movie posters and time, we use a regression Convolutional Neural Network (CNN) to estimate the release year of a movie using an isolated title text image. Due to the difficulty of the task, we propose to use of a hybrid training regimen that uses a combination of Mean Squared Error (MSE) and Tukey's biweight loss. Furthermore, we perform a thorough analysis on the trends of fonts through time.

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“…Even Bayesian learning has been applied to ANNs to improve the performance of real estate pricing models (Del Giudice, De Paola, & Forte, 2017). Also, convolutional-based neural networks have been used (Piao, Chen, & Shang, 2019). Rafiei and Adeli (2016) used deep belief restricted Boltzmann machines to assess whether a company should build or not housing, depending on the predicted housing price.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy

Vargas-Calderón,

Camargo

2020

Preprint

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In many countries, real estate appraisal is based on conventional methods that rely on appraisers' abilities to collect data, interpret it and model the price of a real estate property. With the increasing use of real estate online platforms and the large amount of information found therein, there exists the possibility of overcoming many drawbacks of conventional pricing models such as subjectivity, cost, unfairness, among others. In this paper we propose a data-driven real estate pricing model based on machine learning methods to estimate prices reducing human bias. We test the model with 178,865 flats listings from Bogotá, collected from 2016 to 2020. Results show that the proposed state-of-the-art model is robust and accurate in estimating real estate prices. This case study serves as an incentive for local governments from developing countries to discuss and build real estate pricing models based on large data sets that increases fairness for all the real estate market stakeholders and reduces price speculation.

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Housing Price Prediction Based on CNN

Cited by 37 publications

References 4 publications

An Integrated Approach Based on Artificial Intelligence Using Anfis and Ann for Multiple Criteria Real Estate Price Prediction

An Integrated Approach Based on Artificial Intelligence Using Anfis and Ann for Multiple Criteria Real Estate Price Prediction

Using Robust Regression to Find Font Usage Trends

Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy

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