Climmo is an amalgamation of “climate” and “immobilier” (DE) / “immobilier” (FR) / “immobiliare” (IT). We feel that it captures our mission to provide quality climate data for the real estate sector across Switzerland!

You can incorporate our climate and air quality data into your valuation assessments, complementing 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 present-day property valuations made for lending purposes, and begin to develop an informed view on how future climate change could affect resale values. You could also include our data in the information documents that you provide to prospective borrowers and/or discuss heating, cooling, or insulation measures that individuals could implement to help retain property values under projected future climates.

You can incorporate our climate data into the list of attributes that prospective buyers or renters can search by (or filter on) in your online platforms, complementing existing criteria such as price, living area, presence of a balcony/garden, and travel time to specified locations (e.g., place of work), etc. 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, you will also be able to provide interesting and useful additional information to prospective buyers before, during, or after visits – information that could help inform their eventual decisions! Equally, using our data will enable you to decide which properties to show prospective buyers in case they have specific climate preferences (e.g. cannot tolerate hot climatic conditions where no dedicated interior cooling systems are in place due to medical conditions).

As a prospective buyer, current 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 – including how it varies from where you currently live (if applicable). In this way you will be better prepared for future climate change – and all for a tiny fraction of the property sale price! If you are a buyer with some geographical flexibility, comparing climates across multiple locations could be particularly valuable.

In addition, when combined with other property-specific information (e.g., construction material, age, roof type, type of heating system, etc.), our data can help you estimate how comfortable a property may be to live in, and how high running costs for heating or cooling might be. Similarly, our data could help guide capital investments (e.g., new or additional insulation, triple-glazed windows, etc.) to maintain or enhance your property's value.

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

We are considering extending our Gold package to include metrics with specific temporal characteristics, such as the duration of cold snaps, dry spells, and heatwaves. We may also consider providing additional uncertainty information around the projected future climate data. Finally, when the next generation of official Swiss climate projections – known as CH2025 – are released, we will update our data products accordingly.

Elevation is a dominant control on the climate at any given location. This is because temperature generally decreases with elevation (known as the “environmental lapse rate”). Higher areas also typically receive more precipitation due to the “orographic enhancement” effect. The elevation therefore provides important additional context when evaluating climate conditions at a property. However, other factors – such as atmospheric circulation and its interactions with Switzerland’s generally complex topography – also influence climate on local to regional scales.

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), relative to the pre-industrial period, that is expected by the year 2100. RCPs therefore reflect not only possible future emissions, but also natural sources and sinks of greenhouse gases as well carbon mitigation strategies.

Our data correspond to RCP4.5 and RCP8.5, which represent plausible “medium” and “high” atmospheric greenhouse gas concentration scenarios. Although future atmospheric greenhouse gas concentrations depend on multiple interacting factors such as global policy implementation, business decisions, and technological advances, as so will not follow RCP4.5 or RCP8.5 precisely, providing climate metrics for both offers a realistic envelope 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 by approximately the year 2050. Unfortunately, because the necessary policies and technologies to achieve RCP2.6 are neither widely implemented nor planned at a global level, it is increasingly viewed as an implausible 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. We feel that 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 each from 0 to 100 relative to all other properties nationwide. For example, a property with an annual mean precipitation percentile of 9 is amongst the 10% driest in the country, while a property with an annual mean temperature percentile of 96 will be amongst the 5% hottest.

In all three climate packages, for every climate variable or metric and for both RCPs, we provide these change figures to characterise the difference between the historical (1991–2020) and future (2035–2064) periods. For temperature metrics we provide absolute temperature changes (in °C), while for precipitation and sunshine duration metrics we provide percentage changes. In our Gold package, we also provide percentage changes. All changes may be either positive or negative. For example, a +1°C change in a temperature metric indicates that conditions are expected to be 1°C warmer on average in the future period than in the historical period. A -20% change in a precipitation metric indicates an expected 20% decrease in precipitation.

Historical data are associated with uncertainty, most of which arises from the process of interpolating station measurements onto the regular grid which provides our input data. Another source of uncertainty is associated with how representative the grid cell values are of the precise property locations within them. However, these uncertainties are fairly difficult to quantify reliably.

Uncertainties associated with the future data could be provided more easily. This is because the CH2018 future projection datasets do not provide only one simulation of future climate for a given RCP, but rather provide outputs from several individual models – model ensembles. Due to the slightly different formulations used by each model (ensemble member), a range of outputs are produced. For the time being, 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. We may consider providing uncertainty information in the future.

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.

PM2.5 and PM10 refer to particulate matter that is less than 2.5 and 10 micrometres in diameter, respectively. These particles are formed during various industrial processes, as well as through secondary formation. There are several emission sources (e.g. transport, households, industry and agriculture). They cause disorders of the respiratory tract and cardiovascular system, and increase cancer risk and mortality rate. (Source: FOEN).

Sulphur dioxide emissions are produced by the combustion of sulphur-containing fuels and by some industrial processes. High concentrations can have negative effects on the respiratory tract; asthmatics and people with chronic respiratory diseases are particularly at risk. Sulphur dioxide is also an important precursor for the formation of acid precipitation and secondary particulate matter. (Source: FOEN).

Nitrogen oxides, including nitrogen dioxide, form in the combustion of motor and heating fuels. Road transport is the main source. They are an important precursor in the formation of acid rain, secondary particulate matter and - in combination with volatile organic compounds - photo-oxidants (ozone/summer smog). Nitrogen dioxide is also a causal factor in diseases of the respiratory tract; children are particularly susceptible. (Source: FOEN).

Ground-level ozone is a secondary pollutant that is formed by the action of sunlight on nitrogen oxides and volatile organic compounds. It has negative impacts on human health. High levels lead to irritation of the respiratory tract and lung tissue, as well as damage to vegetation and crops. The data we provide are calculated from half-hourly ozone concentration measurements. For each month, the 98th percentile (i.e., the level which 2% of observations exceed) is calculated. The highest monthly 98th percentile values within each year are then taken. (Source: FOEN). Our ozone graphs also show the corresponding legal limit.

The data we provide for PM2.5, PM10, suphur dioxide, and nitrogen dioxide are annual mean estimates. As with ozone, we show the corresponding legal limits on our graphs. Pollutant concentrations can be considerably higher for specific periods or seasons, depending on sources, external conditions (e.g. weather), and the pollutant in question. In addition, all air quality data we present are derived from computer models, and are therefore associated with uncertainty. Actual concentrations may differ from the values presented.

The underlying PM2.5 data are available on a 100 m resolution grid from the year 2015 to 2023. However, data for the other pollutants are only available at 100 m resolution from 2020 to 2023 (and on a 200 m grid before that). To provide the most locally relevant data, and to avoid potential sharp "step changes" in the time-series associated with the grid change, we elected to only provide the more recent data for these pollutants.