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https://github.com/mehassanhmood/housingmarket-ba

GTA-Housing Market Analysis
https://github.com/mehassanhmood/housingmarket-ba

r rshiny-application

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GTA-Housing Market Analysis

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# Housing Market Analysis and Forecast



[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)  

[![Build Status](https://img.shields.io/badge/build-passing-brightgreen.svg)](https://example.com/build-status)  



## Table of Contents

1. [Overview](#overview)

2. [Features](#features)

3. [Installation](#installation)

4. [Usage](#usage)

5. [Configuration](#configuration)

6. [License](#license)



---

## Overview



This project focuses on the **GTA Housing Market Analysis** and is a final project for my **Master's in Business Analytics and AI**. The aim is to analyze and gain insights into the housing market trends in the Greater Toronto Area (GTA), utilizing historical data up until 2022. This analysis is particularly relevant in the current context of economic uncertainty, where fluctuations in the housing market have been a significant concern.



By leveraging data analysis techniques in R, the project seeks to explore potential changes in housing market trends over time. This includes identifying patterns, correlations, and anomalies that could shed light on the current state of the market. The project also aims to understand the broader economic implications of these trends and how they might inform predictions for the future.



Despite the data only going up to 2022, the analysis is timely due to ongoing shifts in the economy, such as inflation, interest rate changes, and post-pandemic recovery. By comparing past trends with current market conditions (as of 2024), I aim to explore whether the market has shifted or if the factors influencing the housing market have remained constant.



---



### Key Objectives:

- **Explore** and analyze historical housing market data in the GTA.

- **Identify trends** in property prices, demand, and supply over time.

- **Evaluate economic factors** (e.g., interest rates, inflation) that might have influenced housing market dynamics.

- **Predict potential market shifts** based on historical data trends and current economic factors.



---



## Features 

- **Dollar_Volume**:  

This likely refers to the total value of all real estate transactions within a given period. It's calculated by multiplying the sale price of each property by the number of transactions. For example, if 10 houses were sold for $500,000 each, the dollar volume would be $5 million.



- **Average_SP_LP**:  

This stands for Average Sale Price to List Price ratio. It compares the sale price of a property to its original listing price. A value of 1 (or 100%) means that properties are selling for their list price, while a value above 1 means properties are selling for more than the list price, and below 1 means they are selling for less.



- **Average_DOM**:  

This stands for Average Days on Market. It represents the average number of days that properties in the dataset remain listed for sale before being sold. A lower average DOM indicates a faster market where properties are selling quickly, while a higher average DOM suggests slower sales.



---



## Installation



### Prerequisites

1. **Software/Tools Required:** R, R-Studio, R-Shiny.



### Steps

1. Clone the repository:

   ```bash

   git clone https://github.com/mehassanhmood/HousingMarket-BA.git

   ```

2. Navigate to the project directory:

   ```bash

   cd HousingMarket-BA

   ```

## Usage



## Configuration



## License