Executive summary
Most investors start with the same input: a list of properties from a portal. The difference is what happens next. In our work, every listing passes through a structured series of filters and benchmarks before it ever appears in a brief.
- Listings are first normalised: station, walk time, layout, age and size are harmonised.
- Each property is then benchmarked against station-level sales and rent data to derive GRM and implied yields.
- Only after risk flags are assessed and opportunities compared within the same budget band do we construct the final shortlist.
The objective is simple: reduce noise, surface the right type of risk, and present investors with a clear set of options instead of dozens of unfiltered listings.
Step 1 — Raw listings and investor mandate
Every engagement starts with two inputs: the investor's mandate and a pool of candidate listings. A typical mandate might include:
- Budget range and leverage profile
- Preferred layouts (for example 1K/1LDK only, no 2LDK)
- Target wards or lines, and maximum walking distance
- Risk appetite: yield-driven, balanced, or core long-term location
Listings are then collected from public sources matching this broad perimeter. At this stage, the data is noisy: inconsistent station names, missing information and marketing language rather than structured fields.
Step 2 — Normalising the data
Before any comparison can occur, we translate portal data into a clean, internal schema. This is where the same logic used in our tools is applied to the advisory workflow:
- Station names are standardised and mapped to a unique station key.
- Walking times are converted to integer minutes and capped at reasonable thresholds.
- Layouts are recoded into consistent categories (1R, 1K, 1LDK, 2LDK and so on).
- Age is expressed in years; floor area in square metres; price in millions of yen.
At the end of this step, the dataset looks less like a set of marketing pages and more like a research table. Only then can we compare like with like.
Step 3 — Attaching station-level benchmarks
Each property is now mapped to station-level benchmarks: historical sales prices and rent levels for similar layouts, ages and walking times. These benchmarks are built from the same internal engines described in:
Once attached, we can compute a set of core metrics for each listing, including:
- Implied GRM based on estimated market rent for that station and layout
- Indicative gross and net yield after standard operating assumptions
- Discount or premium versus the station's typical pricing for comparable stock
This step is where many listings simply drop out: properties that looked attractive on portal photos may screen as over-priced once compared to their peers.
Step 4 — Risk flags and qualitative filters
Numbers are necessary, but not sufficient. Before a listing can enter a shortlist, we apply a set of risk flags and qualitative filters, such as:
- Unusual building age or upcoming large-scale repairs
- Problematic layout for the target tenant segment
- Overly optimistic rent assumptions relative to surrounding stock
- Complex ownership structures or atypical management fees
These flags do not automatically disqualify a property, but they change how it is presented in the brief and how it should be underwritten by the investor.
Step 5 — Prioritisation and portfolio fit
Once the filtered listings have been benchmarked and risk-assessed, they are ranked according to the investor's objectives. Two investors with the same budget may receive different shortlists because their constraints and preferences differ.
Typical prioritisation axes include:
- Yield versus location quality
- Exposure to a specific line or station cluster
- Balance between stable cash flow and optionality for future repositioning
In many cases, we also look at how each candidate would sit alongside existing holdings to avoid unwanted concentration.
Step 6 — The investor brief
Only after the previous steps do we compile the investor brief. Instead of dozens of raw listings, the investor receives a concise document with:
- A short list of properties, each with clear GRM and yield metrics
- Contextual station-level commentary and comparable benchmarks
- Key risks, sensitivities and suggested underwriting assumptions
The outcome is not a recommendation to buy a specific asset, but a structured map of where the most interesting opportunities lie within the investor's mandate.
Why this process matters
Without a framework, it is easy to over-react to individual listings, photos or asking prices. By treating listings as data first, we make it possible to compare opportunities across stations, layouts and age brackets in a disciplined way.
For investors based overseas, this process also reduces the information disadvantage relative to local market participants and helps focus time and due diligence on the right subset of properties.
Investor takeaways
- Most of the work happens before a property enters the investor brief.
- Normalising data and attaching station-level benchmarks is essential before comparing GRM or yield across listings.
- Risk flags and qualitative filters are as important as headline returns for long-term performance and peace of mind.
- A disciplined pipeline transforms portals from a source of noise into a useful starting point for serious capital allocation.
Next step
If you would like your own mandate to go through this process and receive a data-backed shortlist tailored to your constraints, you can request an investor brief.