Sixty Percent

My job is straightforward: to raise finance for clients in a way that serves their needs. Simple.

But anyone who works in property finance soon notices a pattern. First-tier lenders are often most comfortable at more conservative loan-to-value levels, while higher leverage increasingly moves the transaction towards specialist lenders and different pricing.

At first glance, the reason appears obvious. Lower leverage gives the lender a larger equity cushion if values fall, costs rise or the borrower’s exit weakens. We could stop there, but I do not think that is the whole answer from the borrower’s perspective.

I began to wonder whether there is a deeper mathematical logic behind the preference for more conservative leverage. Not a rule invented by lenders, but a pattern they may have arrived at empirically through repeated exposure to full market cycles.

What connects poker, information theory and property finance?

John L. Kelly Jr. was not thinking about property finance when he developed what became known as the Kelly criterion. He was working at Bell Laboratories on a problem in information theory: if you have an advantage, how much of your capital should you commit to each opportunity to maximise long-term growth?

The insight is simple. Having a favourable opportunity is not enough. Commit too little and you underuse the advantage. Commit too much and volatility begins to work against compounding. Even with a positive expected return, over-commitment can reduce long-run wealth.

That is the part I find relevant to property. Maximising expected return and maximising long-run wealth accumulation are not the same objective. At low leverage they can appear to move together. Beyond a certain point, expected equity return may continue to rise while long-run resilience starts to deteriorate.

The calculation

The model below is deliberately illustrative. It assumes an 8% total property return, annual property-price volatility of 10%, and progressively higher borrowing costs as leverage rises. These are modelling assumptions, not forecasts or statements of current lender pricing.

Under those assumptions, the model peaks at around 65% LTV. The deterioration becomes more pronounced as leverage rises further. By 85%, the apparent equity return remains high, but the estimated long-run growth rate has turned negative.

The important point is not the exact percentage. Change the assumptions and the optimum moves. The point is the relationship: leverage can continue to improve the return shown on paper after it has started to reduce resilience.

There is also a simplification worth acknowledging. Rental income and capital growth are treated as equivalent parts of the total return. In reality they are not. Rental income can service debt each month. Capital growth is unrealised until sale or refinance. That makes cash-generating return more valuable to a leveraged borrower than the table implies.

High leverage has made people wealthy

This needs to be said plainly. High leverage has made some property investors very wealthy. Investors who borrowed heavily before long periods of capital growth, refinanced successfully and redeployed the released equity built substantial portfolios. For some, the strategy worked exceptionally well.

That does not contradict the argument. Probabilities allow winners. The difficulty is that the successful investors remain visible while those who were forced to sell, lost equity or failed to refinance are much less likely to remain part of the conversation.

The people who are not in the room

Property discussion is particularly susceptible to survivorship bias. We hear from the landlord who repeatedly refinanced and expanded. We hear much less from the investor whose leverage left too little room when values fell, debt costs rose or the refinance no longer worked.

Some failures are visible through insolvency. Others are not. A fixed-charge receiver appointment, a voluntary sale under lender pressure or a private settlement may leave a much weaker public trail than a successful portfolio. The absence of those stories can make high-leverage strategies appear more robust than the full distribution of outcomes would suggest.

2008, briefly

At 85% LTV, a 15% fall in value is enough to extinguish the borrower’s equity before allowing for selling costs. A borrower did not have to select the wrong tenant or misunderstand the property for this to happen. The leverage itself removed the margin for error.

A borrower at 60% LTV experiencing the same fall still suffered a material loss in equity, but retained a substantially larger cushion. That difference can affect the ability to refinance, sell in an orderly way or simply remain in position long enough for conditions to improve.

Higher leverage also reduces tolerance for rising debt costs. When interest expense increases at the same time as values weaken, the borrower can be pressured from both directions: cash flow is tighter while the routes to refinance or sale become more constrained.

What the 60% preference really means

The Kelly framework does not prove that 60% is the universally correct LTV for property. It does suggest something more useful: there is a point at which adding leverage can continue to improve expected return while simultaneously weakening long-run resilience.

Experienced lenders do not need to express that through equations. They see losses, recoveries, refinances and defaults across many borrowers and full market cycles. Their credit policies, pricing and leverage limits are practical expressions of that accumulated experience.

There is nothing inherently wrong with leverage. Used sensibly, it is one of the reasons property can be such an effective investment asset. But leverage is not only a return amplifier. It is also a fragility amplifier.

At lower leverage, the borrower has more room to be wrong. As leverage rises, the position becomes increasingly dependent on valuation, timing, liquidity, cash flow and lender confidence.

That, to me, is why sixty percent is more interesting than it first appears. The mathematics does not dictate lender policy, but it helps explain the logic that may sit underneath it.

If you are comparing different property-finance structures, the useful starting point is not simply the maximum loan available. The EAS Finance Workspace can be used to test how leverage, funding cost and exit assumptions alter the wider transaction before a lender is selected.