Every asset and the whole portfolio, simulated through thousands of economic futures. The answer is a distribution rather than a single forecast, measured against whatever the fund is judged on: an index, a composite or a target return.
Book a 30-minute callBoth sides of the number, at asset, portfolio and fund level, so what each holding contributes to risk-adjusted return can be seen and acted on.
In portfolio work presented at the NCREIF conference, measuring and managing diversification lifted mean portfolio IRR by 60 basis points and cut portfolio volatility from 4.67% to 2.67%.
Fund manager, ODCE Fund
Our long-term, 20-year real estate return is nearly 9%, which is a very strong number, but how much risk is associated with that return? If we can achieve the returns we want but do so with less risk, then OPTrust is in a much stronger position to meet its pension obligations. PROMS is the solution that is helping us get there.
Robert Douglas
Senior Managing Director, Real Estate Investments · OPTrust
You might own an office building and be able to assess risk of rents for instance, but how do you determine the tenant risk. If a tenant leaves the building or defaults payments, what does that do to the volatility of returns over time. PROMS is the only platform that allows us to measure that risk.
Fund Manager · Pension Fund
The forward view is the one now being examined. Consultants and investment committees run their own comparisons against the index, and the questions that follow are about the range of outcomes rather than the single number.
An expected return, the range around it, and a stated probability of hitting the number the fund is judged on, over one, three and five years.
Which assets, sectors and lease events carry the downside, and how much each contributes to it.
Sector, market and structural weights tested against the benchmark or the target they answer to, so a deliberate deviation is evidenced rather than asserted.
PROMS Investor takes the same lease-level asset models used across the platform and runs them through thousands of economic scenarios. Each scenario is a coherent path for growth, inflation, interest rates, pricing and tenant behaviour, so correlations hold across the whole book and diversification is measured rather than assumed. The result at every time step is a distribution: the expected return, the range around it, the shape of the downside, and the probability of beating the benchmark.
Two sources of risk, one distribution: where the fund lands against its benchmark, and which assets put it there.
1-in-100 five-year fall is approximately 23%. 1-in-20 five-year fall is approximately 14%.
Portfolio vs. weighted average, showing an illustrative difference of +36bps IRR and -1.05 percentage points of volatility.
An economic model links national economies to property markets, so each scenario is internally consistent: growth, inflation, rates and pricing move together. Asset events run on the same paths, and correlations hold across markets and through time, so the portfolio result is not the sum of its assets.
The probability of beating the benchmark over one, three and five years, and everything behind it: expected return and the range around it, volatility and the tail, and each asset's contribution to portfolio return and risk.
Hold, sell, buy and leverage decisions tested against that distribution before anything is committed, at asset level and at fund level, because the best asset decision and the best fund decision are not always the same one.
The measure a fund is judged on sits inside the platform: an index replicated, a custom composite built, or a target return set. The portfolio is simulated against it, and the output is a stated probability of meeting or beating it over one, three and five years, with the drivers behind it by sector, market and asset. Because every assumption is stated, the number can be defended when it is challenged.
A house view becomes a figure an investment committee can examine and an analyst can test.
The downside comes out of the same run as the expected return: the fund's value in every scenario, at every time step, as the chart above shows. The run also shows which assets, sectors and lease events drive it, and how much each contributes.
A downside you can attribute is a downside you can act on.
Every asset is ranked by its marginal contribution to portfolio return and to volatility, so disposals are sequenced by their effect on the fund rather than their merit in isolation. In a representative portfolio, the analysis showed that disposing of the four worst-ranked assets would lift IRR by 56 basis points and cut volatility by 20 basis points.
A list of disposals becomes a sequence, priced by its effect on the fund.
The same book, decisions evidenced, risk quantified and returns improved.
Thirty minutes with the team to understand what you are trying to solve and where PROMS fits.
Two sources of risk modelled together, with the relationships between them preserved.
A vector error correction model captures how national economies drive property markets, and Monte Carlo simulation layers asset-level event risk on top: tenant default, lease break, void periods. Correlations are preserved throughout, so diversification is quantified rather than assumed.
The measure a fund is judged on, modelled inside the platform and simulated against.
Any published index, custom composite, peer set or absolute return target is replicated in the platform, and the portfolio is run against it to produce a stated probability of beating it over one, three and five years, with the drivers behind that figure. Where a fund is index-relative, sector, market and structural positions are measured against the index's own weights, so the contribution of each deliberate position to return and to risk is separated. Setting up the measure a fund is judged on is part of onboarding.
Each asset shown against what the market pays for the risk it carries.
Every asset is plotted by return against standard deviation, against a risk-neutral line calibrated to the market, with direct comparison to listed equities and government bonds. Assets above the line earn their risk premium, and the distance either side of it is measured. Leverage is applied where it actually sits, so the effect of the fund's own debt on both return and volatility is visible rather than assumed, and gearing can be tested as a position in its own right.
The benefit of the portfolio over its parts, as a number rather than a principle.
Portfolio volatility is measured directly rather than inferred from the weighted average of its assets. In a representative portfolio, diversification added 36 basis points to mean IRR and took 105 basis points off volatility. The same calculation runs by sector and by market, so the holdings doing the diversifying can be identified.
A prescribed path and a full distribution, from the same asset models.
Simulation answers how likely an outcome is. A stress test answers what happens on a path someone else has specified, whatever its likelihood, which is why regulators prescribe them. PROMS Investor runs both off the same asset models and the same assumptions, so the two are comparable rather than the work of separate tools. Prescribed sets are run as issued rather than rebuilt by hand, including the macro scenarios published for CCAR and the stresses used in Solvency II reporting.
A disposal list ordered by what each sale does to the fund.
Every asset is ranked by marginal contribution to portfolio return and volatility, and disposal sequences are tested against the resulting distribution before they are committed to.
Every number can be opened, traced and reproduced, years after the fact.
Every input, assumption and scenario path is kept, and every version and draft is named and date-stamped, so any historic analysis can be reproduced exactly as it stood. Because the inputs and the outputs of each run are both retained, two runs can be compared directly: what changed in the assumptions, and what it did to the result. Every output traces back to the inputs behind it, and all of it exports to Excel.
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