Data and analysis on the Swiss real estate market. Since 2002.

For whom

Pub­lic in­sti­tu­tions, banks and in­surers, real es­tate in­vestors, con­sultan­cies and re­search.

Why

Eco­nom­ists. We are eco­nom­ists. Eco­nom­ists without data are the­or­ists. We are prac­ti­tion­ers. En­gin­eers. We provide the basis for de­cisions – in the here and now.
Data. We ob­tain it. We bring it to life. Com­puter sci­ent­ists, lin­guists, eco­lo­gists – so that you can count on the data.
What mat­ters. People’s wel­fare. That is what drives us. Al­ways.

Who

Daniel Sager, eco­nom­ics, com­puter sci­ence
dsager@meta-sys.ch, 043 322 17 72
Timon Brüll­mann, com­puter sci­ence
tbruell­mann@meta-sys.ch, 043 322 17 73
Ju­li­an Sager, qual­ity con­trol
tick­ets@meta-sys.ch, 043 322 17 70

 

Urban Müller, eco­nom­ics, cli­ents
umueller@meta-sys.ch, 043 322 17 76
Raf­faele Musa, com­puter sci­ence
rmusa@meta-sys.ch, 043 322 17 78
Monika von Flüe, ad­min­is­tra­tion
mvon­fluee@meta-sys.ch, 043 322 17 70

Where

Jo­weid Zen­trum 4
8630 Rüti
043 322 17 70
nautilus@meta-sys.ch

Crawl­ing – com­plete and ac­cur­ate

We col­lect ac­cess­ible pages and doc­u­ments across the in­ter­net, with a focus on com­plete­ness and ac­cur­acy.

We are not im­mune to bot pro­tec­tion, but we have had sev­er­al vac­cin­a­tions.

Scrap­ing – from text to struc­ture

We ex­tract the in­form­a­tion needed from the pages and doc­u­ments, with tra­di­tion­al meth­ods such as reg­u­lar ex­pres­sions, but also with local LLMs.

Polling pub­lic sources – al­ways up to date

We do not only col­lect on the web, but from many other sources as well: via APIs, down­loads from web­sites and so on. As a rule with per­man­ent polling, so that your data never goes stale.

View of in­fra­struc­ture

Using ray tra­cing, we model what can be seen from a given loc­a­tion – and what that means for the value of a prop­erty. A con­crete use case: wind tur­bines and photo­vol­ta­ic in­stall­a­tions.

De­du­plic­a­tion – every ob­ject ex­actly once

Crawl­ing pro­duces many du­plic­ates. We find them with nearest neigh­bours on text em­bed­dings, co­sine sim­il­ar­ity and fur­ther nat­ur­al lan­guage pro­cessing. The para­met­ers weigh up both er­rors: miss­ing du­plic­ates or match­ing them wrongly.

Com­pany names – one com­pany, one entry

Com­pany names come in the most var­ied forms. Our data on real es­tate agen­cies, and our com­pany data in gen­er­al, are linked through this en­tity match­ing.

Ad­dresses – even when spelt dif­fer­ently

Ad­dresses ap­pear in dif­fer­ent spellings, in Biel/Bi­enne even in two lan­guages. Miss­ing or com­bined house num­bers make match­ing harder. A multi-stage pro­cess, using among other things the Leven­shtein dis­tance, en­sures pre­ci­sion.

Ad­Scan

Ad­Scan is our col­lec­tion of all Swiss real es­tate list­ings since 2004: up­dated daily, all cat­egor­ies.

more about Ad­Scan · Re­quest a test data­set

Im­moR­isk

Im­moR­isk is our sus­tain­ab­il­ity rat­ing. It trans­lates cli­mate, en­ergy and mar­ket risks into value sta­bil­ity – from the port­fo­lio view to renov­a­tion plan­ning and op­tion valu­ation.

more about ImmoRisk

Scarcity in­dic­at­or

The scarcity in­dic­at­or is our in­stru­ment for meas­ur­ing scarcity in the real es­tate mar­ket – in use by the fed­er­al gov­ern­ment. It shows not only ex­cess sup­ply, but also the ex­tent of ex­cess de­mand. It also com­bines rent­al and owner-oc­cu­pied hous­ing and so cov­ers the whole mar­ket.

Four-quad­rant model

We have for­mu­lated the four-quad­rant model of Di­Pasquale and Wheaton non-lin­early, quan­ti­fied it and turned it into graph­ics. This makes it pos­sible to play through the ef­fects of mac­roe­co­nom­ic or reg­u­lat­ory changes be­fore they hap­pen.

Spe­cial top­ics

Quant­it­at­ive ana­lyses of your ques­tion, from mar­ket con­stel­la­tions to ten­ancy law: the price ef­fect of en­ergy in­fra­struc­ture, ask­ing rents vs. mar­ket rents, op­tim­al reg­u­la­tion of hous­ing sup­ply and nu­mer­ous as­pects of ten­ancy law.

Wohn­mon­it­or

On be­half of the Fed­er­al Of­fice for Hous­ing FOH, we present the scarcity in­dic­at­or and nu­mer­ous other met­rics in the Wohn­mon­it­or.

wohnmonitor.admin.ch

Im­moR­isk

We con­dense the rat­ings in Im­moR­isk into one con­cise in­dic­at­or. How we go about it is ex­plained on the web­site.

www.immorisk.ch

02.06.2026 - NZZ article - High rents

The art­icle “Tatort Wohnungs­markt: Ist die Zuwan­der­ung schuld an den hohen Mieten? Eine Be­weisauf­nahme” quotes Daniel Sager.
» more

18.03.2026 SRF report - Housing shortage

Daniel Sager ex­plains the find­ings of the hous­ing mar­ket mon­it­or­ing that Meta-Sys AG car­ries out for the Fed­er­al Of­fice for Hous­ing.
» more

14.08.2024 - ImmoRisk

The ana­lys­is of the prop­er­ties rated with Im­moR­isk as of mid-2024 shows that in the Im­moR­isk bench­mark 70% of the sus­tain­ab­il­ity risks have already been re­duced.
» more

14.08.2024 - ImmoRisk

The ana­lys­is of the prop­er­ties rated with Im­moR­isk as of mid-2024 shows that in the Im­moR­isk bench­mark 70% of the sus­tain­ab­il­ity risks have already been re­duced.

Con­versely, there is still po­ten­tial to re­duce 30% of the risks, the highest of them in re­source con­sump­tion.

https://immorisk.ch/en/News
For whom, why, who
Jules Verne
Jules Verne ap­plied his ima­gin­a­tion to the latest sci­entif­ic find­ings. With “Twenty Thou­sand Leagues Under the Seas” he in­spired us.
We have ad­ven­tures in the sea of data and bring the data we cap­ture to life.
Data Collection
Charles Darwin
For five years Charles Dar­win ex­plored the oceans of the world. That laid the found­a­tion for the the­ory of evol­u­tion.
We col­lect data to find pat­terns that can be ex­plained.
Data Matching
Mary Anning
From her fossil finds, Mary An­ning re­con­struc­ted the cor­res­pond­ing in­com­plete and un­known skel­et­ons.
We dis­cov­er the sim­il­ar­it­ies in in­com­plete or im­pre­cise data.
Data Pooling
Carl von Linné
Carl von Linné looked for the com­mon fea­tures in order to clas­si­fy all known liv­ing be­ings.
We as­sign di­verse data to com­mon fea­tures.
Data Modeling
John Stuart Mill
John Stu­art Mill linked so­cial and eco­nom­ic is­sues in a (mostly) pro­gress­ive way.
We model for for­ward-look­ing eco­nom­ic de­cisions and solu­tions.
Data Rendering
The Little Mermaid
The Little Mer­maid does everything to win the prince’s fa­vour.
We do everything to de­light our cli­ents with our visu­al­isa­tions and ap­plic­a­tions.
Blog
News
Our thoughts and ideas on cur­rent eco­nom­ic or tech­nic­al ques­tions. Signed con­tri­bu­tions do not ne­ces­sar­ily re­flect the “com­pany view”. Only con­tro­ver­sial dis­cus­sions bring pro­gress.