On real-estate prices and rental yields

R Jagannathan, in a recent article, predicts an eventual crash or protracted stagnation/decline in real-estate prices in India.

What the aforementioned article presents, as an indicator of a “bubble”, is the differential between rental-yields and secured bank lending-rates. Now, I do not know much about the nitty-gritty details of the real-estate market in India and I concede upfront that a lot of that differential might have to do with market microstructure which may have a lot of inefficiencies.

What I do want to consider more deeply is another factor contributing to this differential – the use of real-estate as collateral for obtaining credit. A few policy implications might stem out of this analysis (it will need some empirical work first to establish if any part of the differential is attributable at all to this phenomenon).

Note that I always label the term “lending” above as “secured”. This qualification is important as secured-lending rates are tied to the performance of the collateral pledged against the loan, without which loan-rates might be prohibitively high or the lending-market may not exist at all (that is, nobody might be willing to lend without collateral).

Now, what is a good piece of collateral? Cash, perhaps – high liquidity, but it is not inflation-protected. Other capital assets like cars, domestic appliances etc, maybe – but they suffer from massive depreciation over time.

How about real-estate? Historically, it does appear to be inflation-protected (contingent on the prevailing credit conditions – more on this later) and can be securitized against rental and household income (either by the owner hosting tenants or the owner saving on rents themselves by living in the property). It typically is also the biggest asset for an average household. These characteristics make real-estate quite attractive as collateral (liquidity is an issue, though).

(Aside, people also talk about gold – I will reserve my comments on that for another post).

The Distortion (?)

So here goes: rental yields are low, lending rates are high – if this is unsustainable, market forces will either drive rents up or house prices down – the eventual result being that rental yields come closer  to secure lending rates. Another possibility is that the prevailing lending rates themselves will come down so that they match rental yields.

Now, the latter can happen in various ways:

  1. The central-bank cuts the short-term secured lending rate causing rates for longer-term lending to go down as well
  2. Overall savings rise leading to lower deposit-rates on offer and competitive rate-cuts in the lending markets as savers hunt for yields
  3. The price of the underlying collateral for secured lending keeps rising – in this case, house prices
  4. General availability of high-quality collateral rises

The list above is, by no means, exhaustive and the causes listed above are not necessarily independent of each other (which, in turn, makes analyzing policy impact that much harder).

Notice point 3 above – it relates to the discussion on collateral earlier. Assets which keep increasing in value (as signified by their price) serve as better collateral and thus, lenders feel more secure in extending credit to the borrowers even to the extent of cutting down their lending rates. This makes rising real-estate prices a self-reinforcing feedback loop (as observed in the US sub-prime lending boom in the 2000s).

However, illiquidity kicks in at lofty prices as the proportion of people in the economy with household incomes high enough to sustain this loop becomes smaller and smaller – leading either to an abrupt bursting of the bubble or a sustained deflation of real-estate prices, as incomes catch up gradually to those lofty valuations.

Also, note again that, in the absence of collateral, lending rates might be prohibitively high or lending may not occur at all. So, in effect, people are ready to pay high prices for collateral in the hope of securing future credit while accepting a low rental-yield in return. For readers more conversant with the terminology of contingent claims, it is the extra premium that buyers pay for the option of getting credit at a lower rate (or at all) in the future. And this premium, in my opinion, is a substantial part of the differential observed between lending-rates and rental-yields (although, as I admit again, this needs to be established empirically for the Indian real-estate markets).

This is quite similar to what people observed as one of the after-effects of the global financial crisis when this phenomenon manifested itself as a flight-to-quality. If good-quality collateral is scarce and capital depreciation, either through (1) default or (2) inflation/uncertain future (national) income, is an issue, then people would be willing to accept low yields (even slightly negative yields!) to safeguard their funding viability.

In India, collateral scarcity appears to be a structural issue – so part of the rental-yield-to-lending-rate differential observed is, perhaps, not indicative of an unsustainable equilibrium but rather of ill-developed financial markets.

Policy implications

If the premium for collateral exists in the rental-yield-to-lending-rate differential, what does this imply for policymakers? Cutting the benchmark-rates may decrease the differential in the real-estate sector but it may lead to capital misallocation and outright bubbles in other sectors. Increasing rates may lead to more savings getting directed to deposits but this may lead to a disorderly fall in real-estate prices making collateralized lending unviable  for many and an eventual decrease in aggregate demand due to the lack of credit. In short, monetary policy is a pretty blunt tool with large and uncertain higher-order effects.

Targeted asset purchases by the central-bank for the real-estate sector may help reduce long-term rates in this sector as lenders know that the central-bank will always create a market for their loans at an attractive price. But the mortgage-backed securities market is virtually non-existent in India. Moreover, this still has the effect of increasing the overall money-supply, which, again, has many higher-order effects.

So, what else? Point 4 above provides a clue – that is, increasing the availability of high-quality collateral. This implies you have three additional tools:

(A) increase the availability of collateral or

(B) increase the quality of the available collateral

(C) decrease the *need* for collateral

(A) boils down to creation of valuable assets. Urbanization and infrastructure development helps in this regard. Even Tier-2/Tier-3 cities have seen a substantial rise in property prices due to the promise of better economic opportunities coming their way with better rail/road/air connectivity and reliable access to electricity and IT/communications. While this may not decrease the differential substantially in highly urbanized areas, it does make collateral more accessible as a lot of migrants in the big cities (or their families back home) do have some assets in their (possibly, smaller) hometowns.

(B) pertains to the quality of collateral. What this implies is the ability to recover some value from the collateral in the case of default on the original loan. The higher the recovery value, the lower the lending rates. Recovery-values depend a lot on asset-liquidity and on the legal framework that tackles insolvency. This implies enhancing liquidity by developing efficient markets for securitized products and ensuring quick resolution of delinquencies and defaults. Both require legislative and regulatory reforms.

(C) requires the development of unsecured lending markets. Again, an insolvency framework helps solve this issue as unsecured lenders are generally considered to be junior to secured lenders and any hope of getting a cut of the recovery value post-default depends on whether the secured lender is able to get their cut efficiently first. Moreover, enhancing unsecured lending/recovery-of-loans for other needs (such as auto/education loans) reduces the need for pledging real-estate as collateral for these purposes.


The differential between rental-yields and secured lending-rates probably has a component attributable to the attractiveness of real-estate as collateral and the scarcity of other assets generally admissible as collateral. Some of the reasons behind this are structural in nature and require the development of better markets for secured and unsecured lending, better frameworks for insolvency resolution and enforcement of contracts. Hopefully, once the government acts accordingly (and it is certainly doing so in many areas), we may see this “distortion” disappear in an orderly manner with the general affordability of real-estate enhanced.

Comment on the Revised Draft Indian Financial Code

My comments on the draft IFC which I sent to the FSLRC:

I appreciate the hard work put in by the Commission to come up with a draft legislation that will aid greatly in enhancing the transparency within the financial system in India. This is a much needed bill and the formation of an MPC as an entity separate from the RBI and the Union Govt is a welcome step in this direction.

However, I wish to make one comment regarding the following clause in the draft code: 

(available at: http://finmin.nic.in/suggestion_comments/Revised_Draft_IFC.pdf )

Chapter 65, Clause 255: 

“Inflation target for each financial year will be determined in terms of the Consumer Price Index by the Central Government in consultation with the Reserve Bank every three years.”

The resetting or review of the inflation-target every three years by the Central Government is undesirable for the following reasons, even if it is done in consultation with the RBI:

  1. Monetary policy actions have indeterminate lags with respect to their effects being actually felt in the economy. A hard review timeline of three years is probably counter-productive and may lead to policy targets being based on noisy data
  2. A major channel through which monetary policy operates is through expectations of future inflation. Adding a three-year review adds uncertainty about the path of the real-rates in the future, especially at the review-tenor. This uncertainty, which is purely an artifact of the clause above, will add an unnecessary term-spread for borrowers in the markets.
  3. It takes time for inflation targets to be institutionalized and the market to adapt to them with savings, insurance and credit products – especially if the products are inflation-linked. The three-year review process will hinder the expansion and liquidity of such markets. Another disadvantage of this would be that market-implied measures of medium-term inflation – such as the breakeven inflation rate on index-linked securities – would be quite useless – which would not be beneficial for the MPC when setting rates.

While an assessment of the inflation target/range an economy should adopt may be necessary from time-to-time, especially for an emerging market like India, it is necessary to:

  • Let an independent body like the MPC decide the target – this will enhance the credibility of the monetary policy
  • Let the target be reviewed over a longer time-span – say 7-10 years – which would allow for short-term aggregate demand/supply shocks to dissipate enough so that potential output and output-gaps can be assessed accurately – based on which an inflation-target/range may be determined

Black-Litterman Portfolio Allocation and Bayesian Analysis – Part 1: Introduction

I have thought about doing an introductory post on Bayesian Analysis for a a while now. There were several real-world applications that I considered – finance, machine-learning, election-forecasting etc.

Among these, the topic I can deal with most thoroughly, without inadvertently misleading the reader (or myself!), is probably finance. And one of the most accessible areas of mathematical finance is portfolio theory.

Beginning with a crash course in elementary statistical concepts, I hope to introduce what portfolio theory is about – the basics of the Capital Asset Pricing Model (CAPM) – where it works and where it fails – both in terms of theory and, more importantly, in terms of application to real-world scenarios.

An introduction to basic Bayesian reasoning will then follow. I do intend to use some concepts from probability and statistics to explain the ideas – but will focus more on the intuition than the mathematics.

I will then introduce a Bayesian take on portfolio allocation – the Black-Litterman model. The emphasis will be on the inherent intuitiveness of the model and how the allocations made using it are more robust than those suggested by CAPM.

Hopefully, this series of posts will be helpful to people who want to design portfolio-allocation frameworks of their own to manage investments (whether as a hobby or in a professional capacity).

So, stay tuned for the next part – where I will introduce some basic statistics and how it can help characterize the various attributes of a financial portfolio.

An Aside on Mathematical Prerequisites

I will use as little mathematics as possible to explain the concepts (referring to more rigorous articles, when necessary). I will also supplement the analysis with Python/R code to help the reader grasp these ideas (because nothing aids the understanding of a concept than writing code that implements it).

As a prerequisite, some familiarity with matrix algebra would be very helpful – as it helps make the notation very succinct and the concepts easier to follow (nothing too advanced, if you know how to multiply matrices together, then you know enough). Some basic calculus and algebra would be useful as well.

I do wish to point out, however, that there is no substitute to actually doing the math to really understand any conceptual model and the assumptions behind it. Intuition helps, but it can only get you so far. My intent here is to ignite your interest in these areas without being daunted and discouraged by the mathematical aspects of the theory at the outset.

It would be a great outcome if you would be willing to explore these areas further in a mathematically rigorous fashion as a result of reading these posts. It might be a bit cumbersome – but I assure you, the rewards, intellectual and otherwise, are worth it.