House Price Prediction using Machine Learning Algorithm - The Case of Karachi City, Pakistan

Ahtesham, Maida; Bawany, Narmeen Zakaria; Fatima, Kiran

doi:10.1109/acit50332.2020.9300074

Cited by 27 publications

(5 citation statements)

References 7 publications

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“…There are some attempts to study housing in Pakistan but those are limited to the valuation of housing (Ali et al , 2015) for city-specific and for the province of Punjab that focused on housing and urban land (Dowall and Ellis, 2009) and they concluded that the market is not organized to perform successfully. The housing attributes were analysed by Pasha and Butt (1996), the city-specific study is conducted by Lodhi and Pasha (1991) focused on Karachi; in a more recent attempt, a study also stressed the rising housing prices in Karachi (Ahtesham et al , 2020) and for Islamabad (Imran et al , 2021); finally housing prices of some cities are analysed by Ahmed et al (2020) and Rehman and Jamil (2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Foreign capital inflow and housing prices in a developing country: an advanced econometric enquiry

Ahmed

Jawaid

2022

IJHMA

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Purpose The study is intended to find out the relationship between housing prices and the inflow of foreign capital in Pakistan. There is a shortage of housing units due to rising population and rural–urban migration since its inception; on the other hand, there is also a lack of housing finances. The urban sprawl has created the demand for housing units, but the supply of housing has not been increased up to the required level, the major reason is a deficiency of housing finances. Design/methodology/approach The analysis was carried out from 1973 to 2018, on an annual, quarterly and monthly basis; the structural changes are captured by the Zivot–Andrews unit root test. Gregory–Hansen test is used for cointegration, the combined cointegration also validates the results. In addition, the rolling window is used to capture timely changes between data sets. Finally, wavelet analysis is used to prove volatility. Findings The rising prices of housing in the country is alarming; Pakistan is a developing country, and it is facing many problems along with a housing shortage. The domestic sources of housing finances are inadequate, so foreign funds are welcomed. The rolling window regression proves that domestic factors along with the foreign capital inflow affect housing prices positively, and the wavelet analysis finds out that foreign direct investment is more volatile than workers’ remittances in financing the housing market. Originality/value This is a pioneering study to find out the impact of foreign capital inflows on the housing prices in the economy of Pakistan. The inadequacy of housing finances from domestic sources attracted foreign funds financing this sector. This study has used new techniques like rolling window and wavelet transformation, such techniques have not been used before.

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Section: Literature Reviewmentioning

confidence: 99%

Foreign capital inflow and housing prices in a developing country: an advanced econometric enquiry

Ahmed

Jawaid

2022

IJHMA

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“…The goal of the paper is to predict reasonable price for customers with respect to their budgets and priorities.  Manasa and Gupta [2] have taken Bengaluru as city for case study. The property size in square feet, location, and its facilities are all key aspects affecting cost.…”

Section: Literature Surveymentioning

confidence: 99%

House Price Prediction Using Machine Learning

2023

IRJMETS

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Machine learning aims at developing self learning algorithms using datasets, such that the machine can be enabled to project future activity based on the past data. It has exhibited impressive developments over the years with the rapid increase in storage capacity and processing power of computers. Taking a case of one such application in real-estate for predicting the price of a house in this project. Machine learning has been employed to help and predict the prices of houses by learning from a sample dataset consisting of attributes which influence cost and other parameters. Firstly, fundamental concepts of a machine learning along with its applications are explored, next in reference to house price prediction, feature assessment, learning techniques and the libraries used for implementation of the project are discussed. Based on the obtained prediction results, performance of the machine learning by linear regression approach is evaluated and conclusions are drawn.

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“…Boosting-based solutions have also been used to tackle the price prediction problem. Extreme gradient boosting (XGBoost) algorithm was used with success to predict house prices in Karachi City, Pakistan (Ahtesham, Bawany, and Fatima 2020). Zhao, Chetty, and Tran (2019) replaced the last layer of a convolutional neural network model for house price prediction with an XGBoost model.…”

Section: Standard Machine Learning Modelsmentioning

confidence: 99%

Describe the house and I will tell you the price: House price prediction with textual description data

Zhang

Branco

2023

Nat. Lang. Eng.

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House price prediction is an important problem that could benefit home buyers and sellers. Traditional models for house price prediction use numerical attributes such as the number of rooms but disregard the house description text. The recent developments in text processing suggest these can be valuable attributes, which motivated us to use house descriptions. This paper focuses on the house asking/advertising price and studies the impact of using house description texts to predict the final house price. To achieve this, we collected a large and diverse set of attributes on house postings, including the house advertising price. Then, we compare the performance of three scenarios: using only the house description, only numeric attributes, or both. We processed the description text through three word embedding techniques: TF-IDF, Word2Vec, and BERT. Four regression algorithms are trained using only textual data, non-textual data, or both. Our results show that by using exclusively the description data with Word2Vec and a Deep Learning model, we can achieve good performance. However, the best overall performance is obtained when using both textual and non-textual features. An $R^2$ of 0.7904 is achieved by the deep learning model using only description data on the testing data. This clearly indicates that using the house description text alone is a strong predictor for the house price. However, when observing the RMSE on the test data, the best model was gradient boosting using both numeric and description data. Overall, we observe that combining the textual and non-textual features improves the learned model and provides performance benefits when compared against using only one of the feature types. We also provide a freely available application for house price prediction, which is solely based on a house text description and uses our final developed model with Word2Vec and Deep Learning to predict the house price.

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House Price Prediction using Machine Learning Algorithm - The Case of Karachi City, Pakistan

Cited by 27 publications

References 7 publications

Foreign capital inflow and housing prices in a developing country: an advanced econometric enquiry

Foreign capital inflow and housing prices in a developing country: an advanced econometric enquiry

House Price Prediction Using Machine Learning

Describe the house and I will tell you the price: House price prediction with textual description data

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