Introduction
Predicting assessed value is a crucial task in the real estate sector. Making informed judgements, such as pricing a property or figuring out its value for taxes, can be aided by accurate assessment value prediction. Machine learning algorithms have gained popularity in recent years for forecasting real estate prices.
We will compare the effectiveness of various models, including lasso regression, ridge regression, multiple linear regression, and Neural Networks. For real estate professionals, estimating a property's assessed value is particularly crucial because it can guide decisions about purchasing, selling, and managing real estate. The usefulness of machine learning models in forecasting the assessed value of properties in Edmonton, Canada, is a topic of this study.
Our models take into consideration a number of factors, such as garage availabilities, neighborhood , location (latitude and longitude), assessment class, distance to center, distance to green spaces, and distance to LRT station.
Because machine learning algorithms can capture non-linear correlations and interactions between variables that conventional regression models cannot, they are attractive for use in real estate prediction. We want to increase the precision of evaluated value estimates in Edmonton using machine learning algorithms. Also, precise assessed value estimates are useful to real estate agents, property owners, and policymakers, this research is significant. Proprietors can, for instance, better understand the value of their properties and make educated decisions about restorations and enhancements with the aid of reliable assessments value estimates. Real estate agents can utilize assessed value estimates to give clients better advice and policymakers can use them to help with zoning and land use choices.
We will compare the effectiveness of various models, including lasso regression, ridge regression, multiple linear regression, and Neural Networks. For real estate professionals, estimating a property's assessed value is particularly crucial because it can guide decisions about purchasing, selling, and managing real estate. The usefulness of machine learning models in forecasting the assessed value of properties in Edmonton, Canada, is a topic of this study.
Our models take into consideration a number of factors, such as garage availabilities, neighborhood , location (latitude and longitude), assessment class, distance to center, distance to green spaces, and distance to LRT station.
Because machine learning algorithms can capture non-linear correlations and interactions between variables that conventional regression models cannot, they are attractive for use in real estate prediction. We want to increase the precision of evaluated value estimates in Edmonton using machine learning algorithms. Also, precise assessed value estimates are useful to real estate agents, property owners, and policymakers, this research is significant. Proprietors can, for instance, better understand the value of their properties and make educated decisions about restorations and enhancements with the aid of reliable assessments value estimates. Real estate agents can utilize assessed value estimates to give clients better advice and policymakers can use them to help with zoning and land use choices.
Research Objectives
This study aims to investigate Edmonton, Canada's use of machine learning methods for property value assessment. This paper will primarily:
1) Analyze Edmonton's current property assessment data and predicting assessed value using various machine learning models. Also, selecting best model which can be used further for property value assessment.
2)Moreover, Factoring which variables are important for the property value and utilizing new variables which will be useful for the prediction.
1) Analyze Edmonton's current property assessment data and predicting assessed value using various machine learning models. Also, selecting best model which can be used further for property value assessment.
2)Moreover, Factoring which variables are important for the property value and utilizing new variables which will be useful for the prediction.
By fulfilling these goals, this study hopes to add to the continuing debates about the best methodologies for valuing real estate in Edmonton and to offer some insight into the potential advantages and restrictions of applying machine learning techniques in this context. The ultimate goal of this research is to assist the creation of fair and equitable real estate valuation and taxation systems as well as to increase the accuracy and consistency of property assessments in Edmonton.