Hotel location has traditionally been measured on four yardsticks: demand (catchment, transport, events), supply (competing room count), land price, and disaster risk. This article quantifies a fifth axis — the operating cost that the climate itself generates every single year — using Japan Meteorological Agency (JMA) climate normals and MetroEngines Research room-rate data. Summer cooling costs have been discussed at length in recent years, but winter heating and snow removal, despite being recurring annual costs, are almost never put into numbers as an input to site selection.

The conclusion first. Across 23 municipalities that lie within Japan’s designated heavy-snowfall zones, normal annual snowfall explains only a quarter of the variance in winter room rates. Within the same snowfall band, towns where the winter rate sinks to 0.62x the summer rate coexist with towns where it jumps to 2.66x. What separates GOP margin and yield is not how much snow falls, but whether the location is equipped to convert snow into revenue.

Snow as Cost or Asset — Defining the Fifth Axis of Location Assessment

According to Japan’s Ministry of Land, Infrastructure, Transport and Tourism (MLIT), the heavy-snowfall zones designated under the Act on Special Measures for Heavy Snowfall Areas account for roughly 51% of national land area and about 15% of the total population. Hotel investment in Japan therefore rests on the premise that, statistically, about half the country is a place where snow falls. Even so, winter operating cost is rarely treated as an independent variable in the investment decision process.

Location assessment already has a fourth axis in wide use: disaster risk, in which flood, tsunami and landslide hazard are folded into the investment decision. A hazard, however, is a one-off event that may or may not occur. The cost imposed by snow and cold is different in kind: it occurs every year without fail, and it concentrates in the season when revenue is thinnest. Because the nature of the exposure is so different, it needs to be treated as a separate axis.

Start by pinning the weight of snow to two numbers. The first is heating degree days (the annual accumulation of how far outside temperature falls below a base), the second is normal annual snowfall. The former governs the energy required for heating and hot water; the latter governs the physical workload of snow clearing, hauling and melting. Both can be computed mechanically from JMA climate normals, so anyone screening a site can obtain the same values.

Snowfall Explains Only 25% of the Variance in Winter Rates — Data from 23 Municipalities

We selected the 23 municipalities for which a weather station’s climate normals are available and for which we could compute an estimated transacted ADR from ten or more properties in both winter and summer. Plotting normal annual snowfall on the horizontal axis and “winter ADR divided by summer ADR” on the vertical, a gentle upward slope is visible. It is true that the snowier the location, the more likely winter rates are to run high.

But the correlation coefficient is only +0.50 and R-squared only 0.25. Three quarters of the variance in the winter/summer ratio is explained by something other than snowfall. In practice, the six municipalities with 900cm or more of snowfall span a 2.5x range, from Minakami at 1.06 to Kutchan at 2.66, and the six in the 400-700cm band span 2.6x, from Aomori City at 0.62 to Hakuba at 1.59.

Listing the municipalities individually makes the structure plain. At the top sit Kutchan (2.66), Hakuba (1.59), Yuzawa (1.53) and Yamagata City (1.41) — every one of them either an international ski destination or home to a large ski area. At the bottom sit Aomori City (0.62), Hakodate (0.64), Asahikawa (0.70) and Niigata Chuo Ward (0.73), the business centres of snow country.

Yamagata City deserves particular note. Its normal annual snowfall of 285cm ranks only 16th of the 23, yet its winter/summer ratio of 1.41 ranks 4th. The Zao demand engine converts a modest amount of snow into a high rate. Minakami runs the other way: with 1,152cm of normal annual snowfall it is among the snowiest places in Japan, yet its ratio stops at 1.06. The sheer volume of snow guarantees nothing about the efficiency with which it converts into revenue.

The Spread in Heating Degree Days Is 18x the Spread in Cooling Degree Days — Climate Cost Is Asymmetric

Next, look at thermal demand itself. Computing heating and cooling degree days from JMA monthly mean temperature normals shows how asymmetric Japan’s north-south spread really is.

Heating degree days run 1,586 for Tokyo’s 23 wards against 4,101 for Asahikawa — a gap of 2,515 degree-days, or 2.6x. Cooling degree days, by contrast, peak at 306 in Osaka City while Asahikawa, Sapporo and Aomori City all register zero, so the gap between the major cities of the Sea of Japan coast and northern Japan on one side and the Tokyo metropolitan area on the other is just 143 degree-days (Tokyo’s own value). The heating-side spread is on a scale roughly 18 times the cooling-side spread.

This asymmetry sits at odds with where the recent debate has placed its weight. Summer cooling costs have been covered repeatedly, and they matter. But on the question of location-to-location disparity, the contest is already settled on the winter side. Cooling costs converge to similar levels almost anywhere in Japan, whereas heating costs open up by more than a factor of two depending on where you build.

Snow adds physical workload on top of heat. Aomori City stood up a heavy-snowfall disaster response headquarters on 29 January 2026 and kept it in place until 25 March (per the city’s own announcement). The city’s fiscal 2025 snow-clearing budget reached a record scale of just over JPY 7.1 billion after supplementary appropriations, and Aomori Prefecture’s total snow-clearing expenditure of about JPY 9.07 billion set a record for the second consecutive year. Those are public-sector burdens, but private facilities operating under the same weather carry their own costs for on-site clearing, roof-snow handling and road heating.

At Prefecture Level, Winter RevPAR Runs 0.53x Summer — Rate and Occupancy Sink Together

At municipality level we could only look at rate. At prefecture level we can bring in the published occupancy rates from the Japan Tourism Agency’s Accommodation Travel Statistics Survey. For calendar 2025, we compare a composite RevPAR built by multiplying the estimated transacted ADR by published occupancy, for winter (January to March plus December) against summer (July to September).

The result is unambiguous. Aomori Prefecture’s winter/summer ratio is 0.53, Akita 0.58, Hokkaido 0.65, Toyama 0.65. Against that, Tokyo runs 1.07 and Fukuoka 1.03 — winter is in fact the stronger season. In snowy prefectures, rate and occupancy sink in winter together. That does more damage to revenue than either falling alone.

Isolating occupancy by property type makes the gap starker still. Tracking business-hotel occupancy month by month through 2025, Aomori Prefecture runs 55.3% in January against 84.8% in August. The gap between the winter average (January to March plus December) of 62.1% and the summer average of 82.4% reaches 20.3 points. Akita follows at 19.9 points, Nagano at 17.7, Niigata at 17.6 and Toyama at 16.9 — the Sea of Japan coast, in order.

The national average seasonal gap, by contrast, is just 3.7 points, and in Tokyo winter actually runs 1.9 points higher than summer. Business hotels in snow country carry a seasonal swing more than five times the national average — and the trough coincides exactly with the period when heating and snow-removal costs peak.

Neighbouring Municipalities Reach Opposite Conclusions — Myoko vs Joetsu, Yuzawa vs Nagaoka

The structure shows up most sharply when adjacent municipalities in the same climate zone are placed side by side.

The Joetsu district of Niigata Prefecture. Myoko (proxy station Sekiyama, normal annual snowfall 1,061cm) has a winter/summer ADR ratio of 1.27. About 25km away in a straight line, in the same district, Joetsu (Takada, 413cm) sits at 0.91. Myoko gets 2.6 times the snow, and yet Myoko is the one where winter rates rise. The ski areas of the Myoko highlands generate winter demand, and snow converts into rate. On the Takada side demand is mainly business travel, and snow appears only as cost.

The Uonuma district of Niigata Prefecture. Yuzawa (1,054cm) at 1.53 against Nagaoka at 0.89. Northern Nagano: Yamanouchi 1.28 against Nagano City 1.03. Hokkaido: Kutchan (921cm) 2.66 against Asahikawa (557cm) 0.70. In every pair the climate zone is the same, and what splits the outcome is the presence of a demand engine, not the volume of snow.

The Annual Cost Gap to Estimate at the Site-Selection Stage — A Modelling Framework

Here we translate the two yardsticks established so far — heating degree days and normal annual snowfall — into money. Actual amounts for any individual property move a great deal with equipment specification and contract terms, so what follows is a relative framework for comparing locations, intended to be used by substituting your own equipment specification and local quotations during due diligence.

On these assumptions, total climate-driven cost runs JPY 13.43 million a year at the reference location (equivalent to Tokyo’s 23 wards), JPY 25.56 million at an Asahikawa-equivalent location and JPY 30.78 million at a Kutchan-equivalent one. The important point here is the cooling-side rebate. In northern Japan cooling degree days fall to zero, so cooling cost disappears and offsets part of the increase in heating cost. After that offset, the net increase is JPY 12.13 million a year for the Asahikawa equivalent and JPY 17.35 million for the Kutchan equivalent.

Climate Alone Moves Yield by 1.45 Points — Sensitivity Analysis

With the same ADR, the same occupancy and the same acquisition price, climate-driven cost alone moves GOP margin from 35.0% to 28.2% — 6.8 points. Converted to GOP yield, that is a fall from 7.47% to 6.02%, or 1.45 points. Even an Asahikawa-equivalent location lands at 6.45%, 1.02 points below the reference.

In a market where cap rates sit in the 5% range, a yield gap of 1.0 to 1.5 points is large enough to flip an investment decision. And yet it is rarely set out explicitly in the acquisition pro forma. The reason is simple: heating and snow removal are buried inside a single “operating expenses” line rather than carved out as an attribute of the location.

From here one question follows that bears directly on the investment decision. By how much would winter ADR have to rise to recover the extra climate cost?

The model property’s winter rooms revenue (December to March, 121 days) is JPY 84.85 million. Recovering the Asahikawa-equivalent net increase of JPY 12.13 million requires lifting winter ADR by 14.3%. For the Kutchan-equivalent JPY 17.35 million, it is 20.5%.

The difference comes not from operating skill but from the demand structure the location holds. Even if the Asahikawa property tried to push through a 14% winter increase, winter demand volume already runs well below summer (business-hotel occupancy across Hokkaido is 74.5% in winter against 84.0% in summer), so lifting rate alone would simply cost more occupancy. In locations that cannot sell snow, climate cost is not something to pass through in price; it is something to compress through capital expenditure and operating design.

Read the other way, that leaves an earnings opportunity in Group B locations. Replacing heat sources for heating and hot water, upgrading insulation and optimising road-heating control all cut costs that concentrate in winter directly, and the payback case is easier to build than for cooling-side measures whose benefit is not seasonally concentrated. Where climate cost reaches 8-10% of revenue, cutting it by a fifth returns the equivalent of 1.6-2.0% of revenue to GOP.

The Ranking of Locations Survives Any Set of Assumptions — Three Scenarios and a Two-Axis Grid

Everything so far rests on two provisional figures: a utilities ratio of 6.5% and a snow-removal unit cost of JPY 100. We now move both at once to see how robust the conclusion is. ADR, occupancy, room count, assumed acquisition price and the reference location’s GOP margin are held constant; only climate-driven cost varies.

Under the optimistic, central and pessimistic cases alike, the order — reference location, then Asahikawa-equivalent, then Kutchan-equivalent — does not change. Even in the most generous case, the Kutchan-equivalent location sits 1.06 points below the reference. In a market with cap rates in the 5% range, a gap that size does not disappear with a different set of assumptions. The winter ADR increase required for recovery moves too, spanning +10.7% to +17.9% for the Asahikawa equivalent and +14.9% to +26.0% for the Kutchan equivalent, but the ratio between the two stays almost flat at 1.39-1.45x. The relative magnitude of climate cost is invariant to the assumptions; only the absolute values move.

Next we decompose how the two inputs — heating degree days and normal annual snowfall — each contribute to yield. Each row’s heating degree days is paired with the cooling degree days that locations at that level actually record.

Read the grid across, and within a single heating-degree-day row, moving from 8cm to 921cm of normal annual snowfall alone drops yield by 1.15 points. Read down, the fall from 1,586 to 4,101 heating degree days is only 0.33 points. What mainly drives the yield decline in Group B is not heating cost but snow-removal cost. In many locations, scrutinising the snow-clearing contract terms and the site geometry pays off more than moving straight to an energy-efficiency capex study.

A reversal also appears: the 2,981 heating-degree-day row beats the reference row (1,586) at 7.53% against 7.47% in the 8cm snowfall column. Where cooling degree days fall to zero, cooling cost disappears and more than offsets the rise in heating cost. Because the reference location splits utilities 40% heating and 40% cooling in equal measure, the reversal holds by construction until heating degree days reach twice the reference, or 3,172. Aomori City (2,981) and Joetsu (2,175) sit inside that boundary; Sapporo (3,420) and Asahikawa (4,101) sit outside it. This is, however, a consequence of the 40/40 split assumption, and the boundary moves if the heat-source mix differs. In practice you would derive the split from your own properties’ actual utility bills and substitute it in.

45 Listed Hotel REIT Properties and 7,314 Rooms Sit in the 12 Heavy-Snowfall Prefectures

This climate cost is already embedded in institutional portfolios. Checking our ownership master for the seven listed hotel REITs (300 domestic properties), the holdings located in the 12 prefectures that contain heavy-snowfall zones — Hokkaido, Aomori, Akita, Yamagata, Niigata, Toyama, Ishikawa, Fukui, Nagano, Gifu, Fukushima and Gunma — come to 45 properties and 7,314 rooms. Of these, 23 properties are in Hokkaido, with three each in Aomori, Niigata, Ishikawa and Gunma.

By sponsor, Invincible Investment Corporation stands out with 26 properties. These are mainly larger limited-service hotels located in the cities this article classifies as Group B: Art Hotel Aomori (211 rooms), Art Hotel Asahikawa (265 rooms), Art Hotel Niigata Station (304 rooms), Hotel MyStays Premier Kanazawa (244 rooms) and others. Ichigo Hotel REIT Investment Corporation holds six properties, Hoshino Resorts REIT five, and Japan Hotel REIT Investment Corporation and Nippon Hotel & Residential Investment Corporation four each.

Verifying this climate cost from the outside, however, is currently difficult. Monthly disclosure by listed hotel REITs centres on revenue-side indicators such as occupancy, ADR and RevPAR; per-property utilities and snow-removal costs are outside the disclosure scope. Invincible Investment Corporation’s published results for June 2026 across 101 domestic hotels show occupancy of 82.7% (down 0.1 points year on year), ADR of JPY 12,412 (down 3.9%) and RevPAR of JPY 10,264 (down 4.0%) — but these are portfolio-wide figures, and the cost structure of the heavy-snowfall properties alone cannot be read out of them.

Which is precisely why estimating climate cost yourself, at the location-assessment stage, is worth doing. Since it cannot be backed out of disclosure, the only option is to model it in-house at the time of acquisition review, using heating degree days and normal annual snowfall as inputs. Fortunately, anyone can obtain identical values for both from JMA climate normals.

A Site-Selection Checklist — Five Things to Look At on the Climate Axis

Summary

Comparing estimated transacted ADR between winter and summer across 23 municipalities in heavy-snowfall zones, the correlation between normal annual snowfall and the winter/summer ADR ratio came to +0.50, with R-squared of 0.25. Snow volume explains only a quarter of the variance in winter rates. Within the same snowfall band the ratio scatters across a roughly 2.5x range, from 0.62 in Aomori City to 2.66 in Kutchan.

At prefecture level we confirmed a structure in which rate and occupancy sink together. Aomori Prefecture’s winter RevPAR is 0.53x its summer level, Akita’s 0.58x. Measured on business-hotel occupancy, Aomori’s winter-summer gap is 20.3 points, more than five times the national average of 3.7 points.

The spread in thermal demand is asymmetric. Heating degree days open up by 2,515 between Tokyo’s 23 wards at 1,586 and Asahikawa at 4,101, while the cooling-degree-day spread stops at 143. The heating-side spread is on a scale roughly 18 times the cooling side, and on the question of location-to-location disparity the winter side dominates.

In the 150-room model, climate-driven cost alone moved GOP margin by 6.8 points and GOP yield by 1.45 points. Recovering that increment through winter rate would require a 14.3% increase at the Asahikawa equivalent, whereas the observed winter ADR runs at 0.70x summer. Kutchan, by contrast, needs +20.5% and posts 2.66x — recovery with room to spare.

That is the case for putting a fifth axis into location assessment. Climate cannot be changed, but the cost climate generates can be quantified before acquisition. Feed in two public datasets — heating degree days and normal annual snowfall — and even a model holding ADR and occupancy constant shows, in advance, that yield moves by more than a point depending on where you build. And whether a location can absorb that gap through rate turns out to differ even between neighbouring municipalities.

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