Most Indians I have met either already own a house, or very much want to own a house. Their dream house may have one room or ten, but there seems to be a pervasive cultural belief that life is incomplete, even unsuccessful, without having a house of one’s own. This belief shows up in many ways — in lunch hour conversations, discussions at home comparing relatives, in the marriage market, in movies, in sayings such as “roti kapda aur makan” (“bread, cloth, and house”). Hence the “bauble” in the title — the ultimate shiny object!
Now I am not sure where this cultural imperative comes from, nor if this is peculiar to India. Is owning a house an essential status symbol for the Indian middle class family? Or does living in a rented house somehow stunt the soul? I don’t know. Perhaps searching for answers to such questions could be a nice line of research for psychologists or anthropologists.
However, what we could do in this article is to look at the purely financial issues with respect to buying a house versus renting a house. To be more precise, buying a house on mortgage vs renting a house. My strategy for doing a fair comparison is as follows: consider all the cash flows involved in buying a house against a loan, all the way to when the loan gets paid off, and then calculate the internal rate of return based on the salable value of the house at that future time. Next, we consider the alternative, that is, take on rent the house that was bought in the last scenario, and track cash flows over the same period of time. To keep the comparison fair, we invest any surplus cash flows available in the rental case. That is, if the rental scenario requires N Rupees less outflow per month, we put N Rs per month in investment. These two simulations are run until the mortgage ends, then compare the internal rates of return for the two scenarios.
It should be immediately obvious that there cannot be single answer to the “buy vs rent” question; it all depends upon the values of the numerous parameters involved. The decision is fuzzier because many of the parameters are related to the future and cannot be known today. But I believe it will be useful to run this model through some set of numbers and get a better feel for the shape of the problem. If you have a different set of values for these parameters for your specific situation, you can use this spreadsheet to generate your own analysis.
Let us begin by listing the input parameters:
Buying a house
- Present cost
- Margin money (down payment)
- Stamp duty and levies
- Interior & Furnishing cost
- Mortgage term (n years)
- EMI value (cash outflow per month)
- Income Tax benefit on loan interest
- Property appreciation % pa
- Yearly expenses (i.e. property taxes, society charges)
- Inflation in yearly expenses % pa
Renting a house
- Deposit amount
- Rent amount (cash outflow per month)
- Income Tax benefits on house rent
- Rental inflation rate % pa
Investing the surplus
- Returns % pa
- We ignore costs of running the house, such as electricity payments, that are assumed to be the same in both scenarios.
- For a fair comparison, the two scenarios should use equivalent, preferably identical, houses.
- To keep the calculations simple, it is assumed that the various % rates stay stable over the whole duration. Alternatively, take these to be the average % rates over the tenure of the simulation.
- Although mentioned above for the sake of completeness, I have left out the impact of income tax benefits from this analysis for several reasons: tax calculation is complex; benefits are capped at fairly low levels; cannot predict how the caps will change in the future. Both sides get benefited, so I feel the inaccuracy from ignoring this factor will be small.
Now, setting out to get good estimates of all these input parameters from the internet was not as easy as I expected. Here is a brief description of how the data was collected.
Historical property rates are difficult to find. I had to combine property rate indexes from several sources that span different periods into a single chart shown in Figure 1, and that too has a span of only 13 years. The government has now started publishing a residential property index (RESIDEX) for major cities, so we should get good data going forward. The average annual appreciation for Mumbai works out to 15.4%. This is based on nhb.org data. Corresponding graphs for other cities are quite different, see Figure 2, also based on nhb.org data, that starts at 2007. Figure 2 indicates that Bengaluru and Hyderabad are in doldrums: zero if not negative appreciation with respect to 2007. Mumbai, Delhi, Ahmedabad, Kolkata are around 15% since 2007, while Chennai property appreciated at a strong 30%, at least up to 2014.
Figure 1. RESIDEX for Mumbai, 2001-2014
Figure 2: RESIDEX chart for several cities, 2007-2014
Rental Rates and Rent Appreciation
Historical rental data is even harder to find. The one consistent message from several sites is this: these days, annual rental yields relative to property price weigh in between 2.5 to 3.5%, although historically the yields have been said to be as high as 7-8%. A real estate agent in Pune tells me that rent appreciation is closer to 5% per year overall, notwithstanding the typical 8 or 10% rent escalation clause found in multi-year leave and license agreements.
CPI inflation has been computed for past 50 years and this is readily available, see my previous post. We take an average inflation rate of about 7.5%.
Margin Money, EMI Calculation and Mortgage Term
Major loan houses seem to ask for 15-25% margin money. I have taken 20% as representative. So for a 125 Lakh house the margin money is 25 Lakh and Loan amount is 100 Lakh.
Most sites are wary about disclosing EMI calculations to anonymous visitors, but several housing loan aggregators peg the current house loan rate for major loan houses to 9.5-10.5%. Luckily for us, ICICI has a nifty EMI calculator where you can input loan amount, interest percentage, and tenure of loan. I have used this to generate some EMI values:
Loan Amount 100 lakh, Interest rate 10%, Tenure 15 / 20 years: EMI 1,07,460 / 95,500 Rs. Alternatively, one can use the PMT function in a spreadsheet:
PMT(rate, intervals, principal) → EMI
Investment Rate of Return
Different sites give different figures, some claiming as high as 35% pa. But I would estimate long term post-tax SIP returns on debt and equity to be around 10% and 15% respectively (see mutualfundinda and jagoinvestor). So we will go with a conservative mixed 40/60 portfolio figure of 13%. I will also run the calculation with a more aggressive equity allocation, considering a young couple could take on more risk.
Yearly Expenses on House
Major recurring expenses on the self-owned house are property taxes and society maintenance charges. Again, these vary widely, good data is not available, but a rough estimate for property tax would be around 0.5% pa of property value (e.g. pcmc doc here). Property tax amounts for a given house increase somewhat with age, but erratically. In absence of any data at all, let us just take a wild guess, say half the CPI for property tax appreciation (3.7% pa).
Society maintenance charges vary a lot based on society expenditure levels and amenities offered. We make a guess again, say 0.75% of initial property value. I think it is safe to assume that maintenance charges will follow inflation (7.5% pa).
Stamp Duty and Levies
There is stamp duty calculator for India. That gave the following values for a 125 Lakh Rs house in Maharashtra:
Stamp Duty : 6,25,000 Rs
Registration Fees: 30,000 Rs
Local Body Tax: 1,25,000 Rs
Total: 7,85,000 Rs (i.e. ~6.3%)
Interior and Furnishings
Sky’s the limit for interiors! But considering that the house is being bought on loan, probably a more prudent budget can be assumed. So a sort of “livable, nothing extravagant, but long lasting” kind of interior would likely take around 10-20% of the property value. We take 15% as a nice round number for our calculations. This is based on feedback from two practicing interior designers-cum-architects, by the way.
Now that we have arrived at a certain set of starting parameters, some from good data, but many from educated guesses, it is time to do the analysis!
Figure 3: IRR variation with change in upside of buy (property appreciation) or rent (investment returns)
The analysis is available here for you to experiment with (but see the disclaimer at the end). The results can summarized as follows:
- For BUY, if the upside (property appreciation rate) is higher than the downside (mortgage interest rate), net IRR is positive.
- For RENT, if the upside (investment returns) is higher than the downside (rental yields),net IRR is positive.
- For same % values of upside, BUY is ahead of RENT by 0-3% (see Figure 3).
- Alternatively, if you can make more with investments than from property appreciation by a few percent, RENT wins.
Although these results were contrary to what I expected before beginning this analysis, a little further thought makes it clear why BUY has a small advantage over RENT:
Provided property growth rate is higher than mortgage interest rate, delaying repayments is to your advantage, because your property begins to appreciate from day one while you repay over next 15 years. In the case of rent, investment effectively as an SIP (Systematic Investment Plan) does not have this advantage. In addition, since the actual rent outflow is higher than expenses on self owned house, this reduces amounts available for investing, dragging down the returns somewhat further.
One issue that is not brought out by the numbers is the question of risk. In the BUY case, you are taking on a single big risk. It is difficult to predict the future. If your income stops due to economic disruptions, or due to health issues or disability, the house itself may be lost.
Therefore, it is prudent to not commit all your disposable income for mortgage payments. So perhaps the best strategy may be, in a sense, a mixed one – that is, buy a smaller house with affordable EMIs and grow the remaining disposable income with proper investments.
Disclaimer: This Buy vs Rent analysis is based on lots of guesswork, forward-looking estimates, and data picked off the internet — use for real-life decisions at your own risk!
Acknowledgments: Thanks to M/s Vrushali Kale and M/s Anita Bhinge for inputs regarding interior costing. A big thanks to Manas Sathe for reviewing the post and helping polish the spreadsheet.