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The aim of this project is to provide the best counties/areas in the USA to invest in for a national real estate developer, individual buyers, banks looking for a place to development of new apartment building or to purchase. Another goal is to predict the house prices in a county in the next few months. Price prediction using Machine learning algorithms XGBoost and Linear Regression considering factors such as Median income in a county, Crime rate in that county, public schools, hospitals, hospital ratings and unemployment rate in the county. Module moved to noteworthy Python libraries and the usefulness of libraries such as Pandas, Numpy, Seaborn, and Matplotlib.
Marjorie Lawrence
Purchasing a house is a big decision in a person’s life and needs a considerable amount of thought and research. One would like to buy a house at the best rate and minimum risk and would like it to be the best investment for the future. Various online websites, real estate agents and realtors try to guide home buyers by letting them compare different houses available for purchase. The unprecedented upheaval caused by the coronavirus will inevitably shift priorities and perspectives. Perhaps most profoundly, it could change how we all think about physical space and how it is shared with others. Whatever short-term contortions the property market goes through in response to the economic devastation wrought by this virus, real estate investors should not lose sight of long-term changes in behavior. These will inform how people live and work in the future, ultimately shaping the types of developments that are most desirable in a world that will never be quite the same. Nowhere is this truer than in real estate investing, where technology, demographics, climate, and affordability are just some of the factors quickly reshaping how and where people live, work and play. Will secondary cities that have historically been overlooked but are attractive to dynamic young populations become popular? How will commercial real estate be affected by telecommuting and declining automobile ownership? Will climate change render waterfront property worthless or spur innovations that enable a different relationship to aquatic environments? These and many other questions are being pondered by investment committees worldwide.
Based on house prices predicted one can invest in real estate, find a county house better suited for their needs where they can buy a house. House buying and selling decision would become easier with the prediction done by this data science project. The stakeholders for the type of project will be:
a) Customers and Real Estate Agents — The real estate industry has long operated according to its own traditions, but the availability of huge volumes of data is revolutionizing the way the industry works. Big data analysis techniques are creating a new real estate market in which both customers and agents are better informed than ever before.
b) Companies — such as Zillow and Trulia can use this analysis to calculate an estimated value of the price that the home might attract based on factors like local schools, crime rates, income level, hospitals etc. and decide marketing strategy.
c) Banks — It’s not just consumers who are using big data to inform their house buying and selling decisions. Banks are also drawing on vast pools of data to predict the risk that a particular mortgage application could pose, using information about both the value of the home and the applicant’s financial situation and history. In addition, banks are also avoiding losing out on foreclosure and short sales, as big data is helping them to predict the maximum sale value that the market can bear.
is 2021 a good year to buy a house? Where should one buy a house? Will secondary cities that have historically been overlooked but are attractive to dynamic young populations become popular? How will commercial real estate be affected by telecommuting and declining automobile ownership? Will climate change render waterfront property worthless or spur innovations that enable a different relationship to aquatic environments?
Process of data cleanup in jupyter lab using python eliminating unnecessary data.
http://localhost:8888/lab/tree/gwu-virt-fin-pt-04-2021-u-c/03-Projects/Project-01/Picture1.jpg
Maplot JupterLab (phyton) Monte carlo Plotly
To predict house prices using supply-demand features, three main datasets have been used.
Federal Reserve Interest Rates (1954 - 2017) Unemployment Rate by County in the USA (2000 - 2018) Zillow economics data (County_time_series and Crosswalk)
Data.gov US Census Zillow Public APIs Awesome-APIs List Medium APIs List
The future of real estate will be driven by new technologies, buyer-agent relationships, and changing homeowner demographics. Real estate investors hoping to achieve success will need to learn to thrive amidst these changes. With new software to speed up closing timelines, online listing sites resulting in more informed buyers, and new age groups entering the real estate market, the industry is transitioning in big ways. Even expert predictions suggest impending market changes. All in all, when it comes to the future of real estate, investors have a lot to look forward to.