Author: Xinbin Huang
Shiny App for real esatate analysis in the USA
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Buying house is one of the biggest decisions for most of the family. Also, the price of the real estate market has increased a lot in recent years. If we could understand the trend of housing price and geographical difference, it would help family and investors to make better decisions. Therefore, I propose building a data visualization via the shiny app that can help investors and family to visually explore a dataset of house values and housing price. My app will allow users to explore the change in housing price for the past a few years in different regions and for different types of properties by filtering and ordering on different variables in order to get an insight of the housing market.
I will be visualizing a dataset of housing prices in the U.S. provided by an online real estate database company, Zillow. The variables that would be used in the visualization include regions (cities), house price value index (as an indicator of price), years, and different categories of houses/apartments.
Bin is a tech company manager of a public listed U.S. company who just got married last year. He plans to buy a new house for his family and may also consider investing in the real estate market, so he wants to know the recent trend of housing price in different regions in the U.S. in order to make a good decision. He wants to explore a housing price dataset that can be automatically visualized by choosing different years range, regions, and housing types to identify the potential good deals. When Bin uses the "Housing Price app", he will have an over of all the available variables, with the range of the years, different categories of housing types, and also regions to include. He can use the slider to change the year's range and pick the cities and housing categories that he wants to include to compare. After exploring the dataset and visualization, he gets a better understanding of the housing market in the U.S. and has identified several places and housing types for further investigation.