Report on House price data project in the United States

The statistical analysis for house price change in the United States shows 6.47%, 32.86% and 28.18% increase in 1 year, 5 years, and 10 years respectively (Globalpropertyguide. com, 2019). The main concern of the project is to explore the dataset of house price changes by county. The analysis will be beneficial in evaluating the financial crisis. In the thesis, the research outcome will explain why some countries are fared better than others and provide valuable support to the theory, data and research. The initial step is to collect data on house prices from the website and then find results through data analysis process (Iley & Lewis, 2007).

Analysis of House price data project in the United States

The research results show the overall crisis of the housing market in the United States that are plunged in the country for recession. The data collected in the analysis is showing house price change from 2012 to 2018. The median house price is used to measure and increase in 2018 as compared to increase in price growth from 2012. The smaller amount in the data shows a slowdown of the home price growth. The increase values are different from each other. The affordability index for the selected cities of the United States is also considered in the analysis (Noguchi & Poterba, 2007). The analysis also considers the percentage of annual income change in the median-priced home. The analysis shows that New York is the least affordable city of United States. While on the other hand, the most affordable city of the United States in Detroit. Different metro areas of the United States are selected in the analysis and the affordable prices are considered for the homes in the United States. The home price change in different cities is considered to represent metropolitan statistical areas (Kiplinger. com, 2019). The data collected for the analysis in the present report is mentioned in table 1.

Table 1: Data collected for analysis of house price change

Percentage change in the peak values

The total number of observations in the analysis is 20. The multiple R is 0.028, R Square is 0.0008, and standard error is 3.47. The regression values and residual values are 0.17 and 217.8 respectively. The significance values are 0.9. The t-test analysis shows 95% upper and lower values as 8.09 and 4.62. The p-value is .

The total number of observations for the lower value of house price change in the analysis is 20. The multiple R is 0.711, R Square is 0.506, and standard error in lower value of house price change is 2.44. The regression values and residual values are 110.3 and 107.6 respectively. The significance values are 0.0004. The t-test analysis is used for the 95% upper and lower values and the values are 4.8 and 0.57. The p-value is .

 Conclusion of House price data project in the United States

In the present report, regression analysis is carried out to evaluate the change in house prices in the United States. The change in the price is shown for four years from 2014 to 2018. The increase in house prices is causing financial crises to the economy of the world. Across the world, the prices of houses are reduced to 34% during the six-year from 2012 to 2018. The American cities index trans are considered to measure the post-recession.

References of House price data project in the United States

Globalpropertyguide. com. (2019). Home price trends. Retrieved from www.globalpropertyguide.com: https://www.globalpropertyguide.com/home-price-trends

Iley, R. A., & Lewis, M. (2007). Untangling the US Deficit: Evaluating Causes, Cures and Global Imbalances. Edward Elgar Publishing,.

Kiplinger. com. (2019). Home Prices in the 100 Largest Metro Areas. Retrieved from www.kiplinger.com: https://www.kiplinger.com/tool/real-estate/T010-S003-home-prices-in-100-top-u-s-metro-areas/index.php

Noguchi, Y., & Poterba, J. M. (2007). Housing Markets in the United States and Japan. University of Chicago Press.