Climmo is an amalgamation of “climate” and “immobilier” (DE) / “immobilier” (FR) / “immobiliare” (IT).

We feel that it perfectly captures our mission to provide quality climate data for the real estate sector across Switzerland!

You can incorporate our current and projected climate data into your valuation assessments, alongside the various property-specific (e.g., floor area, age, condition, etc.) and location-specific (e.g., noise levels, proximity to public transport, shops, and schools, etc.) attributes that you already use.

In this way, you can refine the present-day property valuations you make for lending purposes and begin to develop an informed view on how climate change could affect future resale values.

You could also include our climate metrics in the information documents provided to prospective borrowers and/or discuss with owners the heating, cooling, or insulation measures they could implement to help retain value under projected future climates.

You can incorporate our climate data into the suite of attributes that prospective buyers or renters can search by or filter on, alongside existing features such as property type (house, apartment, etc.), price, floor area, presence of a balcony/garden, and travel time to user-specified locations (e.g., place of work).

This ensures prospective buyers – many of whom are increasingly climate-conscious – are as well-informed as possible before attending an in-person viewing. In a competitive market, this could offer you a distinct advantage.

As an estate agent, using our data will enable you to provide unique and important additional information – specifically regarding recent and projected future climates – to prospective property buyers before, during, or after visits. As a result, their decisions will be more informed.

Our climate information could also be incorporated into the search criteria available on your websites and/or be used internally to help find the perfect property for clients with specific needs (for example, individuals who cannot tolerate hot climatic conditions without dedicated cooling systems).

As a prospective or current homebuyer, owner, seller, or renter, our data will enable you to gain an immediate understanding of the historical and projected future climate at properties of interest – all for a tiny fraction of a typical property sale price!

If you are a buyer with some geographical flexibility, the ability to compare climates across locations and timeframes could be particularly valuable.

In addition, alongside other property-specific information (e.g., construction material, age, roof type, heating system type, etc.), our data can help indicate how comfortable a property may be and what the running costs (e.g., for heating/cooling) could be like.

As a homeowner, our data could help guide capital investments (e.g., new or additional insulation, triple-glazed windows, etc.) that may help maintain or increase your property's value in the future.

No, not at present, although we may consider extending our offering in this direction in the future.

We are currently developing a front-end for private customers. A web map interface with an address search bar will allow users to select up to four properties of interest. According to the selected data package (Gold, Silver, or Bronze), interactive graphics will be automatically generated to summarise and compare past and projected future climates at these locations. These graphics will also be downloadable.

Once complete, we will consider providing additional uncertainty information around the projected climate futures and explore extending the Gold package metrics to include those with specific temporal characteristics, such as the duration of cold snaps, dry spells, and heatwaves.

We also intend to expand our offering to include recent air quality information.

Finally, when the next generation of official Swiss climate projections – known as CH2025 – is released, we will update our data products accordingly.

Elevation is a dominant control on climate at any given location. Temperature generally decreases with elevation (known as the “environmental lapse rate”), and higher areas often receive more precipitation due to “orographic enhancement”.

Providing elevation therefore gives important additional context when assessing climate conditions at a property. However, other factors – such as atmospheric circulation and its interactions with Switzerland’s generally complex topography – also influence local to regional-scale climate.

A representative concentration pathway (RCP) is a possible future atmospheric greenhouse gas concentration trajectory or scenario. RCPs are named according to the changes in radiative forcing (measured in watts per square metre, W/m²) expected by the year 2100, relative to the pre-industrial period. As such, RCPs reflect not only possible future emissions but also carbon mitigation strategies and natural sources and sinks of greenhouse gases.

Our data are based on RCP4.5 and RCP8.5, which represent plausible “medium” and “high” atmospheric greenhouse gas concentration scenarios. While future atmospheric greenhouse gas concentrations are unlikely to follow RCP4.5 or RCP8.5 precisely – as they depend on multiple factors such as global policy implementation, business decisions, and technological advances – providing climate metrics for both offers a realistic range of future possibilities.

RCP2.6 assumes immediate, rapid, and drastic reductions in greenhouse gas emissions, leading to global “net zero” (balanced emissions and capture/drawdown) being achieved around the year 2050.

However, because the necessary policies and technologies to achieve RCP2.6 are neither widely implemented nor planned at a global level, it is unfortunately no longer considered a plausible climate pathway.

According to the World Meteorological Organization (WMO), 30-year periods are standard for computing stable and representative “climate normals” (averages). The period 2035–2064 is centred on 2050.

In our view, 2050 represents a reasonable time horizon for considering climate change in the context of the real estate sector. We may be able to provide data for other future periods upon request.

In our Bronze package, we contextualise the annual mean climate statistics (for temperature, precipitation, and sunshine duration) at each property, for both the historical period and the two future scenarios, by calculating their respective percentiles. This involves ranking them from 0 to 100 relative to all other properties across Switzerland.

For example, a property with an annual mean precipitation percentile of 9 will be among the 10% driest in the country, while a property with an annual mean temperature percentile of 96 will be among the 5% warmest.

In all three packages, for every climate variable or metric and for both RCPs, we provide these change figures to describe the difference between the historical (1991–2020) and future (2035–2064) periods. Absolute temperature changes (in °C) are provided for temperature metrics, while percentage changes are shown for precipitation and sunshine duration metrics. These changes may be either positive or negative.

For example, a +1°C change in a temperature metric indicates that average future conditions at the property in question are expected to be 1°C warmer in the future period than in the historical period. A -20% change in a precipitation metric indicates an expected 20% decrease in precipitation between the same periods.

Percentage changes are also provided for the Gold package metrics.

If a given metric is not available at a particular property under future conditions – for instance, due to data limitations or modelling constraints – this will be clearly stated in the output.

The historical data are associated with uncertainty, a large proportion of which arises from the process of interpolating measurements made at climate stations onto the regular grid which we use as input. There is also uncertainty associated with how representative the grid cell values are of the precise property locations within them. However, such uncertainties are fairly difficult to quantify.

Uncertainties associated with the future data could be provided more easily. This is because the CH2018 future projection datasets do not merely provide a singular simulated view of future climate for a given RCP, but rather provide a suite of outputs from several individual models – known as model ensembles. Due to the slightly different formulations used by each model (ensemble member), ranges of outputs are produced.

In the first version of our data, however, we provide only a central estimate by selecting, for each variable (i.e. temperature or precipitation) and RCP scenario, the ensemble member that is deemed representative of models with moderate climate change sensitivity (deviation from historical conditions), according to the “Ensemble Sub-Selection tool” of MeteoSwiss. In all cases, we use “All of Switzerland” as the selection region.

Relative sunshine duration is calculated as the actual duration of sunny conditions observed on a given day, divided by the maximum possible sunshine duration (astronomical day length).

For example, if the astronomical day length is 10 hours, but four of those hours were cloudy, then the relative sunshine duration would be 60%.

We recommend the free and open-source software QGIS for desktop GIS users.

We may also be able to provide dedicated consultancy support to help you integrate our data into your company's workflows and products, including by providing data visualisation capabilities, using tools such as R, Python, or PostgreSQL / PostGIS.