Data Study on Financial Decisions in NYC
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Link to app: https://enzo-borja.shinyapps.io/buy_value/
An embedded video of my presentation can be found at the bottom of this post.
Motivation
Whether to buy property or continue renting in New York City is a financial decision with significant impact. Renting may appear more affordable on a month-to-month basis. However, there may be long-term financial benefit to buying property even if it can be capital-intensive. This app is meant to be an access to data and tool for New Yorkers to factor in their own financial situation in making this important decision.
Methodology
This app will helps the user answer the following questions:
- What is the inventory of properties out there I can afford that is better than renting?
- What is the most expensive property I can afford (herein referred to as "best buy option"), and what will my recurring costs be?
- If I buy my best-buy option, after how many years will I be better off than if I had continued renting?
Data
| Buying | Renting |
|
|
The data used for this model is a list of tax class 2 properties sold in Manhattan in 2019. Financial attractiveness is than evaluated within a 30-year horizon, which is a common mortgage lifetime.
In this model, we are making predictions on market distribution based on what 2019 looked like. How the coming years actually turn out could be different, so it is important to keep in mind that this model is only an estimate. Nevertheless, this model is most useful in evaluating how sensitive the rent-or-buy decision is to the provided parameters.
Another key assumption is that user has enough saved for a down payment. In the rent option, this down payment value is assumed to be instead invested in alternate securities.
Source of Data
Data was taken from the New York City Department of Finance (link). Only the Manhattan data was used. From this dataset, only tax class 2 condos and co-ops were used.
Inputs in Modelling
There are several inputs used in the modelling.
- Monthly housing budget: the amount of money you have available for either buying or renting. Any money left over after monthly rent or mortgage is assumed to be invested in alternate securities (i.e. index funds in the stock market).
- Alternative monthly rent: how much you pay in rent monthly. The model will analyze properties that are more affordable than rental options with this monthly rent.
- Mortgage rate: the annual rate your mortgage increases in value. This is usually the number that banks quote you. The monthly mortgage rate used in the model is calculated as (annual) mortgage rate / 12. There is some error in doing this, but it is not significant.
- Income tax rate: determined by your income level. This will be used to calculate how much of a tax refund you get because mortgage interest payments are tax-deductible.
- Down payment: The fraction of the sale price you pay at the time of purchase. A lot of properties require 20% minimum. Most banks have penalties for paying less than 20% down. Therefore, the model limits down payment to above 20%.
- tax rate as percent of market value: calculation of the actual tax rate is complicated in New York City. To make the model simpler, use a tax rate suggested in this StreetEasy article.
- common charges as percent of market value
- Property value annual growth rate: the average annual rate the market value of propertie increases.
- Alternative investment growth rate: the average annual growth rate of other investments such as stocks.
- Closing costs: fees paid to brokers, lawyers, and taxes at time of purchase.
Price to Value Ratio
To help make a comparison between rentals and purchases, I used a price-to-value ratio (P/V ratio) over a 30-year period. P/V ratio is defined as the amount paid divided by the value of assets. The amount paid is simply the housing budget over 30 years. This table helps explain the P/V ratio:
| buy | rent | |
|---|---|---|
| price | monthly housing budget*12*30 | monthly housing budget*12*30 |
| value | future portfolio value of alt. investments and of property | future portfolio value of alt. investments |
For the rent option, price includes cash going to rent, and left-over cash going into alternative investments. For the buy option, price includes cash going to mortgage, taxes, and common charges, and left-over cash going into alternative investments.
Analysis
The model takes the inputs and calculates a P/V ratio for all properties and outputs those that are within the monthly housing budget. A distribution of affordable buy options by P/V ratio is reported in figure 1. For comparision, a P/V ratio of the rent option is also provided.
Figure 2 plots P/V ratio vs. sale price. The best buy option is the property in this distrbution that has the highest sale price. The monthly mortgage, tax and common charges of this option is reported. At this point, questions 1 and 2 can be answered.
The financials of the best-buy option is further evaluated throughtout a 30-year period. Figure provides total personal value over the years for each scenario. For the rent option, total personal value is the value of the investment portfolio. FOr the buy option, it is the sum of investment portfolio and property value minus mortgage debt.
Figure 5 shows the cummulative cash out over the years.
- rent option: - (rent payments)
- buy option: - (interest + taxes + common charges) + tax refund
Figure 6 takes the ratio of data from the previous figures to explore how worthy each option is through the years.
Data Analysis
Default case
In the default case, there is a strong case to buy. Based on 2019 data, there should be a good number of properties in the market that are better than the rent option. It is also observed that the monthly housing budget is more limiting than the financial attractiveness of buy options. In other words, if you can afford to buy something, then it is highly likely that it is better than renting.
In the default case, the best buy option is a market value of $541,250 with monthly mortgage of $1,870.21, monthly taxes of $676.56 and monthly common charges of $451.04. Figures 4, 5 and 6 show that from the beginning, the buy option not only results in higher value but also requires less overall cash.
Sensitivity analysis
Let's say that we want to buy more expensive property without increasing our monthly budget. We can then look at properties that have a significant tax abatement such that the tax rate would only be 0.05%. Perhaps we may also receive a gift fund from parents that would enable a down payment of 50%. In this case, the best buy option shoots up to $988,000 with monthly mortgage of $2,133.69, monthly taxes of $41.17 and monthly common charges of $823.33.
What or how much to buy can be sensitive to the property value growth rate and the alternative investments growth rate. The the property value growth rate is below the alternative investment rate, figure 2 has a positive slope which suggests it is better to buy as cheap as possible. However, when the property value growth rate exceeds the alternative investment rate, the it would be more financially attractive to buy as expensive as one can afford.
Finally, we can try to make a case for renting by adjusting the alternative investment rate. By setting this to 11% while the property value growth rate is at a default 4%, the financial picture is the same at the end of 30 years. This will be shown on figure 6. If the alternative investment rate increases further, then renting becomes more attractive sooner.
Conclusions
In general, if one can afford to buy, then it is better to buy. However, buying property requires significant capital cost for the down payment and closing costs. There is also the unquantified value of flexibility. When one buys property, then they are geographically less mobile to move to a different location that could perhaps provide a higher net income. This model also does not consider the quality of living and of the properties themselves. In general, this app should help the user provide bounds to their different financial scenarios and understand the sensitivity of their decision to the right factors.
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