Using AI in real estate can provide insights and faster analytics into properties listed under their book value.

• Predictive Analytics: Machine learning(ML) algorithms can analyze historical data to predict future property values based on previous market trends. This help investors make informed and data driven decisions in regards to buying, leasing, or selling properties that are on the market today. Large data is need to help models preform accurately.

• Identify Opportunities: ML can go through large datasets to identify opportunities and assess potential risks associated after acquisition. Features into a property such as location, renovation dates, age of buildings, number of rooms/bathrooms, square feet, lot size, and property condition, service as prossible indicators for future investments.

• Decision-Making: ML can automate complex tasks that take up a professional valuable time, like assessing spreadsheet data, calculating return on investment prospects, allowing investors to take on more opportunities. Despite the results that ML can provide, it is important to review AI's output. The algorithm is not the all-in-one solutions for real estate investing.

• Identify Overlooked Features: AI-powered algorithms can identify and target demographics that can be drivers in valuation. Such as a waterfront property by a lake or a downtown condos with a view.

The data is provided by Shiva Chandel. https://www.kaggle.com/datasets/shivachandel/kc-house-data Online property companies offer valuations of houses using machine learning techniques. The aim of this report is to predict the house sales in King County, Washington State, USA. The dataset consisted of historic data of houses sold between May 2014 to May 2015.

In relationship to price, square feet living space is highly postively correlated with price.

Because of a couple of outliers with in the correlation, ML models can experience difficulties in predictions.

Since correlation does not mean causation, a test of importance in relation to price is needed to ensure better accuracy in prediction target variables (price in this analysis). This data suggestions that in King County, Washington, USA, the most important things to consider when investing in a property are grade, square feet of living space, and latitude(location). A property with a low listing price with high numbers in these areas could pose as a valuable investment.

In regards a higher preforming model, MSE & R^2 is used. Depending on what is important to an investor, either can serve as benchmarks. Overall the random forest model can designated as the best preformer and the model to predict real estate prices.

• R^2: Is used to evaluate how good of fit of a regression model to the data. It measures the proportion of variance in price that is explained by the features in the model. R-squared does not measure predictive accuracy or causality. There for it is used in par with other models to reach a decision.

• MSE: Evaluates the accuracy of ML models. It measures the average squared difference between the actual values and the predicted values. Lower MSE indicates a better model performance, it signifies that the predictions are closer to the actual values. The linear regression is the most accurate in this analysis.

Among the linear regression, random forest and SVR models tested, Random Forest can indicate what are the key driving factors for price. The Linear Regression drafted should be used to suggest the most precise predictions of prices for future investments. SVR underpreformed in all 2 metrics, making it obsolete for helping investors make a real estate decision.