Property Pricing Trends - Brooklyn, NY
Property prices in Brooklyn show some interesting trends. There are different contributors that could make the cost of a property higher or lower. They include the location, the size of the lot, as well as other factors. The question for people seeking invest in property is: how to find the best value?
About the App
1. Renovations Toll of Sale Price
First, we can look at the years when buildings were built or renovated once or twice. There is a spike in the 1930s, which could be due to Roosevelt’s becoming President. To promote the American economy during the Great Depression Roosevelt launched various building campaigns. It is possible that the trend we see is the result of some of Roosevelt’s improvements. After that big spike, there were some other times when building and renovating occurred, but not to the same extent. For example, in the past year, we see there were some new buildings. However, as there is not as much room for us to build as there was back in the 1930s, I do not think we will achieve the same scale of development. Notably, renovations appear to occur at about the same rate as building. Consequently, in years of more building, there is also more renovating.
Here the analysis is based on the sale price and renovation, and one graph has the year the building was built as well. These two graphs show the same relationship.
From these graphs, we can conclude that, for the most part, the very expensive buildings did not have any renovations. Renovations do not appear to have much impact on the increase in the cost of a building relative to another. If it is an older building. then it may make a big difference, but otherwise, it does not seem to have a significant impact on the sale price.
2. Sale based on Location
Sometimes sales are not only based on how big the popery is but where the property is as well.
This map is divided up based on zip codes throughout Brooklyn, NY. It shows that the areas near Manhattan have the highest prices. It could be only because of that reason or that there are more buildings there and that causes the properties to be higher in price, as well. In the middle, there are also higher priced properties, but that is basically just houses that are a higher price.
3. Tax class based on Location
Different kinds of properties/buildings have different tax classes. Tax classes 1-3 are for houses, while tax class 4 is for buildings, hotels, factories and stores. Tax class 2 means that the house has more than 3 units in it. First I made a bar graph showing the different tax classes based on the price range of the property. I had to subset some of the data because some were included with the price range of 0-50e6 that you could not see any of the other price ranges. Most properties are either in group 4 or 2. It seems like some buildings, factories or stores are cheap because many fall into the cheapest range and very few in the most expensive. This shows that maybe the very expensive buildings are really outliers.
The second graph is a map from Leaflet. Its colors represent the different tax classes based on location/zip code. Tax class two seems to be in every location. Tax class one is showing up with the more expensive area, which is really interesting. Tax class four is in basically every location, which makes sense because there are stores, hotels and buildings in every location. Tax class three only shows up in one spot, which makes it not useful for comparisons.
From here we can see, tax classes make some difference in the price of a property, though not a very substantial difference. It is very interesting to see where the tax classes are located and what price range is the highest in each tax class, though, in this context, it does not give such great insight.
About the Dataset
I got a dataset from Kaggle that had different properties in Brooklyn. I then grouped them by zip code so I could use Leaflet to graph the data points. It helped visualize the data more clearly.
If a row had more than 3 NAs in it, I got rid of it. This was not the biggest deal because I had way over 10,000 rows, so then I just kept 10,000 after the rows with NAs were eliminated.
I would love to further expand on and use different parts of the data set that I did not yet get to use. There were 40 columns in the dataset, and I only used a few of them.
Also, I would love to have split the dataset based on buildings and houses because I'm sure I could see more trends with that done. I feel like the houses got a bit swallowed up because buildings are super expensive. It would be interesting to see if the houses were more expensive in places with fewer buildings. which would change how the leaflet graphs looked.
In conclusion, there are many factors that contribute to the price of a house/building. Certain factors are more important than others. From this analyses, we could help people see trends in areas so that they can make an informed decision about investing in a particular area that is cheaper at the moment. On the other hand, they may want to build somewhere that is the cheapest and then others might follow them and it might grow into a big city. Big cities don't develop overnight, but if people are willing to invest for the long term, they can see substantial returns.