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House sales in KingCounty, USA.

Market Analysis and Investment decisions

Objective

This project aims to analyze King County house sales data to identify factors influencing sale price and provide potential sellers with strategies to increase their home's value. Data cleaning and linear regression will be used to uncover insights.

Data
Skills

  • Sourcing the open data

  • Exploring the relationships through exploratory visual analysis.

  • Compare different Python libraries for data visualization.

  • Analysing geographic variables using choropleth maps to draw early insights.

  • Differentiate between predictive analytics, machine learning, and predictive modelling.

  • Conduct a regression analysis in Python and interpret the results.

  • Cluster analysis in Python.

  • Unsupervised machine learning.

  • Sourcing & Analysing Time Series Data.

  • Presenting final data Using Dashboards using Tableau.

Tools

  • Python(Matplotlib, statmodels, Heatmap etc.)

  • Tableau

Factors impacting property prices

Kingcounty1.png

Variables relate to size of the house like “bathrooms”, “bedrooms”, “sqft_above”,” sqft_living15” and the quality of the house like “grade”, “view” have strong correlations with price.

Kingcounty2.png

The heatmap shows that the higher prices commanded by the houses are relatively closer to the water(water front).With houses in Seattle and Bellevue, the most expensive areas are in the North showing the correlation with Latitude.

Variables with strong correlations 

Price Vs. Grade

Kingcounty6.png

Most of the houses are graded at 7. These houses may be of reasonable quality with affordable price.

Price Vs. Waterfront

Most of the houses are not with waterfront. But, the houses with water frront seem to be an advantage and  adds value to the house.

Kingcounty7.png

Price Vs. Number of floors

Kingcounty8.png

The houses with most sales are with 1 or 2 floors. Houses with more floors has higher than the average price, which make sense has they provide more space for living.

Price Vs. Condition

Most of the houses shows the average condition of 3 and 1 & 2 with less price and poor quality.

Kingcounty9.png

Conclusions & Recommendations

Leverage key predictors

Homeowners should prioritize improvements to features with high predictive power, such as: grade, condition, square footage, location, waterfront views.

Data-driven approach

Homeowners should utilize online resources and data analysis tools to research comparable sales in their neighborhood to gain a better understanding of local market trends and property values.

Market Research

Staying informed about local market trends, interest rates, and economic conditions is crucial for making informed decisions regarding homeownership.

Longterm Perspective

Homeowners should consider long-term value appreciation when making improvements and renovations.

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