Forecasting Real Estate Market with Linear Regression & LSTM

Learn how to forecast real estate market trend with linear regression and LSTM (Long Short Term Memory) Model

Created by: Christ Raharja
Ex Technology Risk Consultant, and E-commerce enthusiast

What Will I Learn?

• Learn basic fundamentals of real estate market forecasting, such as getting to know market characteristics and major problems faced by real estate market
• Learn how to perform linear regression calculations and gain an understanding of regression coefficients, intercepts, dependent and independent variables
• Learn how to forecast real estate market trend using linear regression model
• Learn how to forecast real estate market trend using LSTM (Long Short Term Memory) model
• Learn how to evaluate the accuracy and performance of the forecasting models using R-squared analysis and directional symmetry analysis
• Learn several factors that can potentially impact real estate market, such as population growth, job market, and infrastructure development
• Learn how to analsze property price trend by calculating its annual mean and median
• Learn how to find correlation between property price and property type
• Learn how to analyse real estate market trend and find investment opportunities using sales ratio calculation
• Learn how to clean datasets by removing rows with missing values and duplicates
• Learn how to detect potential outliers in the dataset

Requirements

• No previous experience in real estate market forecasting is required
• No previous experience in linear regression is required
• Willingness to learn and conduct a lot of experiments with big dataset

Target audience

• People who are interested in forecasting real estate market using linear regression and LSTM model
• People who are interested in analysing and visualising complex dataset using Pandas, Matplotlib, Numpy, Seaborn, and Scikit Learn
• People who are interested in real estate investment