The city of Vancouver, located on the west coast of Canada, is known for its astonishing natural beauty, vibrant culture, and booming real estate market. The housing market in Vancouver has been one of the most active and competitive in North America in recent years, with prices fluctuating dramatically and demand consistently overshadowing supply. As such, it is crucial for real estate professionals, home buyers, sellers, and even casual spectators to have access to up-to-date information on housing prices and trends.
A dashboard that compiles data on housing prices and presents it in an interactive and informative format can help all stakeholders keep track of the market and make more informed decisions. For example, sellers can use a housing price dashboard to see how their home compares against similar properties in the area, determine an appropriate listing price, and monitor how the market shifts. Buyers, on the other hand, can use a dashboard to compare prices across neighborhoods, track changes in market conditions, and identify the best deals that fit within their budget.
A housing price dashboard can be useful for more than just buyers and sellers. Researchers and policy-makers can also benefit from such a tool, as it can help them understand the dynamic housing market and make evidence-based decisions. Additionally, members of the public who are curious about the state of the housing market can use a dashboard to gain insights into trends and patterns over time.
This dashboard aims to provide all these stakeholders with a valuable resource for monitoring the housing market in Vancouver. By compiling data on housing prices from the City of Vancouver Open Data Portal and presenting it in an interactive and engaging format, we can help users stay informed about the state of the market and make better-informed decisions. The dashboard proposed will be user-friendly and interactive, and will feature a variety of visualizations to help them make sense of the information presented. For example, users can select the specific community, house type, price range, and year built that they are interested in. Then, they will receive a geographic map, a histogram of housing prices, a snippet of the sample dataset, and a bar chart of housing prices for different property types generated according to their preferences. By combining all of these visualizations together, users will gain a better understanding of what the actual housing market looks like based on their interests. They can adjust any feature to get different results that match their needs. Ultimately, the goal is to create a comprehensive and informative dashboard that can serve as a valuable tool for anyone interested to know more about Vancouver's housing market.
The data set we are going to visualize is the data set contains information on properties from BC Assessment (BCA) and City sources from 2020. There are 871,053 observation in this data set. Each data point has 29 associated variables that describe the property.
We are not going to use all the variables in the visualization. The LEGAL_TYPE, which has three categories (land, strata, and other). Additionally, the ZONING_CLASSIFICATION define what kind of zone the property is located. The most common zone classifications are comprehensive development, one-family dwelling, and multiple dwelling. Moreover, the data set contains four years records, the variable PEPORT_YEAR give information about the year (2020, 2021, 2022, and 2023). The records for each year are quite balanced. There is also some information about property identification (PID, FOLIO, LAND_COORDINATE), and specific location information (ZONE_DISTRICT, STREET_NAME, PROPERTY_POSTAL_CODE). Using the above variables, we will also calculate the average property tax given different type of property.
Note: The dataset we are working with is very large, which presents a challenge for us in terms of loading and processing it. Currently, we are using a pre-loaded version of the dataset, but we do acknowledge that there are some missing values, which may affect the quality of our analysis of the housing market, as well as the visualizations presented to potential users. We will continue to work on addressing the incompleteness of the data collection by implementing further imputation methods.
Anthony, a software engineer, is preparing to relocate to Vancouver with his family in order to take advantage of a new employment opportunity. Although he has never been to Vancouver or is familiar with its real estate market, he wants to buy an apartment there. He hopes to be able to [explore] a dataset in order to learn important trends about the state of the local real estate market. In order to find the best fit for his budget, he wants to [compare] housing costs across various neighborhoods, construction years, and property types.
Anthony can obtain current data on homes by using the "VanHouse App," which has four windows that display the map, the histogram, the bar chart of housing price and a sample data source. To find the right match, he can use the toolbar on the left to select the exact community, house type, price range, and year built. Anthony can get a better idea of where to reside in the city by utilizing the "VanHouse App" to collect information on Vancouver's cost of living. Even if he never intends to invest or relocate there, he can still keep up with real estate trends and developments in Vancouver. By following changes in the city's real estate market, he can stay informed about macroeconomic trends and make better decisions about his future investments and finances.