How view deter­mines well-being and val­ue

With a dig­i­tal twin of Switzerland’s build­ing stock, we are able to derive a range of view-met­rics, such as a building’s visu­al share of a lake. This allows us to mea­sure the finan­cial impact of visu­al ele­ments on hous­ing prices at a pre­vi­ous­ly unat­tained lev­el of res­o­lu­tion. We find that large views of lakes and cities in the far dis­tance have the strongest impact on the sale price; how­ev­er, we find the finan­cial influ­ence of the stud­ied visu­al ele­ments are high­ly-con­text depen­dent. The analy­sis pro­vides a rich pic­ture of how visu­al qual­i­ty varies across the Swiss build­ing stock and with­in a giv­en build­ing.

View in Real-Estate

View qual­i­ties are com­mon­ly under­stood to play an impor­tant role in how indi­vid­u­als per­ceive land­scapes and how they make deci­sions. Recent neu­ro­science research high­lights for instance the uncon­scious influ­ence of frac­tal pat­terns on visu­al per­cep­tion and well-being. Opti­mal frac­tal dimen­sions (i.e. nature scenery) great­ly reduces stress and induces the release of endor­phins (Briel­mann et al., 2022). Fur­ther, dis­tant back­grounds (>1km) tend to com­mand a greater share of people’s atten­tion than objects in the mid-ground (150–1km) (Hull & Stew­art, 1995). Both the aspect of depth per­cep­tion and vis­i­bil­i­ty of frac­tal pat­terns (such as trees) are thus rel­e­vant aspects to con­sid­er in the con­text of the built envi­ron­ment. This is backed up by urban health and indoor research indi­cat­ing that a high-qual­i­ty win­dow view improves a work­ers men­tal state and sleep qual­i­ty, reduces stress, and boosts cre­ativ­i­ty (Al Horr et al., 2016; Frontczak & War­goc­ki, 2011).

What we see not only influ­ences our well-being, but can also have an effect on rents or real-estate val­ue. View and visu­al qual­i­ty met­rics explain price dif­fer­ences in Gene­va mul­ti-fam­i­ly (Baranzi­ni et al., 2008), as well as Man­hat­tan office rentals. In the lat­ter case, office spaces with high access to views had a 6% net effec­tive rent pre­mi­um com­pared to spaces with low view access (Turan et al., 2021).

While pre­vi­ous analy­ses have high­light­ed the revealed pref­er­ence — high­er pre­mi­ums — for high-qual­i­ty win­dow views, they have, for the most part, focused on small­er case-stud­ies, rely­ing on sim­pli­fied proxy vari­ables. Thus, with the new abil­i­ty to quan­ti­fy the view with ori­en­ta­tion-spe­cif­ic and 3D-based met­rics, we have the oppor­tu­ni­ty to ana­lyt­i­cal­ly eval­u­ate the finan­cial val­ue of a view at a high­er res­o­lu­tion and with larg­er geo­graph­ic cov­er­age.

Our view data: Derived from a Dig­i­tal Twin

Dig­i­tal twins or sim­u­lat­ed 3D urban envi­ron­ments allow researchers to cap­ture infor­ma­tion based on ele­va­tion and ori­en­ta­tion, enabling a more com­pre­hen­sive and quan­ti­ta­tive def­i­n­i­tion of a view. Our view data­base con­sists of 32 mio. view­points from build­ing facades across Switzer­land. Each point rep­re­sents the visu­al land­scape as seen through a vir­tu­al win­dow, allow­ing the quan­tifi­ca­tion of a view in terms of the com­po­si­tion and con­fig­u­ra­tion of visu­al ele­ments.

Con­crete­ly the Dig­i­tal Twin con­sists of an exact 3D rep­re­sen­ta­tion of all build­ings and topolo­gies, view-rel­e­vant point of inter­est data, such as moun­tains, nuclear pow­er plants, pow­er trans­mis­sion lines, and dif­fer­ent land sur­face types such as roads, for­est, lakes etc. This allows us to mod­el what is vis­i­ble from dif­fer­ent floors from every side of the build­ing. One view­point con­tains the infor­ma­tion about the share of facades, roofs, lakes, veg­e­ta­tion and unde­sir­able struc­tures, such as high-volt­age pow­er trans­mis­sion lines.

Visu­al land­scape ele­ments can vary in scarci­ty. For instance, a mea­ger 4.6 % of the Swiss build­ing stock has a 1% view onto a water­body (e.g. riv­er, lake); where­as 57.6% has a 1% view of nature. Abun­dance of such visu­al ele­ments fur­ther varies by loca­tion; for instance, the aver­age build­ing in Basel will have a 18.7% view of veg­e­ta­tion, com­pared to 26.7% in Gene­va.

Urban form and nat­ur­al ter­rain play a sig­nif­i­cant role in deter­min­ing the local sup­ply of views. If we con­sid­er the coastal cities of Lau­sanne and Zürich, and exam­ine sole­ly the dis­tri­b­u­tion of lake-views; a few inter­est­ing pat­terns emerge: Build­ings in Lau­sanne are not only more like­ly to have lake-views, but also tend to have a larg­er visu­al share if they do. How­ev­er, there are pro­por­tion­ate­ly more build­ings in Zürich with excep­tion­al­ly good lake-views (total share of over 10%), indi­cat­ing a high­er degree of inequal­i­ty with respect to access to lake-views across these two cities. Con­sid­er­ing the loca­tion of the build­ings with the best lake-views, it becomes evi­dent that, while the hilly topog­ra­phy of the north-east­ern shore of Lake Gene­va (Lau­sanne, Lavaux, Vevey, Mon­treux) increas­es the propen­si­ty of above aver­age views far away from the lake, the clus­ter­ing with­in 1.5 km of the lakeshore in Zürich cre­ates a nat­ur­al scarci­ty of high-qual­i­ty lake-views.

Eco­nom­i­cal­ly, scarci­ty of goods typ­i­cal­ly results in a high­er will­ing­ness to pay which may con­tribute to the com­pet­i­tive­ness of Zürich’s hous­ing mar­ket. These obser­va­tions are of course intu­itive, how­ev­er, the abil­i­ty to quan­ti­fy the nation­al sup­ply of views opens up new avenues to mea­sure our cities and improve the accu­ra­cy of prop­er­ty eval­u­a­tion mod­els.

Plot­ting the geo­graph­ic foot­print of lake-view build­ings, col­ored by the size of their respec­tive largest view, reveals the dif­fer­ences in sup­ply of lake-view build­ings, between Lake Gene­va and Lake Zürich.

View influ­ences Home prices in Switzer­land

We exam­ine the effect of dif­fer­ent visu­al ele­ments in Switzer­land on house prices. Specif­i­cal­ly, we define our set of view attrib­ut­es as the max­i­mum vis­i­ble share of a select­ed vari­able for a giv­en build­ing; e.g. a 5% lake-view. Con­trol­ling for stan­dard struc­tur­al, accessibility‑, and envi­ron­men­tal char­ac­ter­is­tics, we use the hedo­nic pric­ing mod­el (Rosen, 1974) to deter­mine the implic­it price of our attrib­ut­es of inter­est. Under this mod­el, we find that homes with a larg­er view of a lake com­mand an 11% pre­mi­um on aver­age; where­as larg­er views of nature, on aver­age, trade at a 1.6% dis­count. Yet, a clos­er look reveals that while par­tic­u­lar visu­al fea­tures, (i.e. lake-view or view of a city in the dis­tance see fig­ure), have large price effects that are glob­al­ly true, oth­ers vary spa­tial­ly and are high­ly con­text-depen­dent. For instance, larg­er views of nature trade at a 1% pre­mi­um in wealthy-urban areas; as opposed to a 1% dis­count in the peri-urban neigh­bor­hoods of mid-sized cities.

The x‑axis shows the high­est val­ue (share as %) per build­ing of the respect­ing view ele­ment. The y‑axis shows the pre­dict­ed val­u­a­tion (in thou­sands of CHF) of a sin­gle fam­i­ly home with a giv­en visu­al share, while con­trol­ling for oth­er pre­dic­tors used in our hedo­nic mod­el. Left: Mar­gin­al effect of a view of build­ing ele­ments in dis­tance (1km). Right: Effect of lake-view on trans­ac­tion prices.

From macro- to nano-loca­tion

When describ­ing the loca­tion of a prop­er­ty, the dis­tinc­tion between macro- and micro-loca­tion has been estab­lished. In Switzer­land, the macro-loca­tion, i.e. the large-scale spa­tial clas­si­fi­ca­tion, is usu­al­ly rep­re­sent­ed by the munic­i­pal­i­ty. The micro-loca­tion describes the small-scale loca­tion char­ac­ter­is­tics that dif­fer­en­ti­ate with­in the macro-loca­tion.

In addi­tion to the iden­ti­fi­ca­tion of a loca­tion and the assign­ment of the asso­ci­at­ed loca­tion char­ac­ter­is­tics, the con­ver­sion of this col­lect­ed infor­ma­tion into eco­nom­ic cat­e­gories is of deci­sive impor­tance. In this con­text, the val­ue is derived from the scarci­ty of the spa­tial bun­dle of goods: the rar­er a cer­tain com­bi­na­tion of desir­able site char­ac­ter­is­tics occurs in an area, the high­er its eco­nom­ic val­ue is in prin­ci­ple. Thus, land with a lake-view fetch­es sig­nif­i­cant­ly high­er prices than land with­out one, because it is rare – at least in Switzer­land. A micro-loca­tion cri­te­ri­on only becomes price-rel­e­vant if it is also an exclu­sive attribute that is not avail­able in oth­er loca­tions in the same macro-loca­tion.

With the grow­ing avail­abil­i­ty of high-res­o­lu­tion spa­tial data, a third lev­el of loca­tion qual­i­ty has found its way into the val­u­a­tion prac­tice: the nano-loca­tion. This term is used to define the qual­i­ty of loca­tion of an apart­ment with­in the build­ing. E.g. an apart­ment on the top floor has dif­fer­ent views than an apart­ment on the ground floor and a south-fac­ing apart­ment has more dai­ly sun­light than a north-fac­ing one. The nano-loca­tion intro­duces an addi­tion­al spread of will­ing­ness to pay with­in the price lev­el of the macro- and micro-loca­tion. Our view data­base allows for data-dri­ven assess­ment of the nano-loca­tion.

Ref­er­ences

Al Horr, Y., Arif, M., Kaushik, A., Mazroei, A., Katafy­giotou, M., & Elsar­rag, E. (2016). Occu­pant pro­duc­tiv­i­ty and office indoor envi­ron­ment qual­i­ty: A review of the lit­er­a­ture. Build­ing and Envi­ron­ment, 105, 369–389. https://doi.org/10.1016/j.buildenv.2016.06.001

Baranzi­ni, A., Ramirez, J. V., Schaer­er, C., & Thal­mann, P. (2008). Intro­duc­tion to this Vol­ume: Apply­ing Hedo­nics in the Swiss Hous­ing Mar­kets. Swiss Jour­nal of Eco­nom­ics and Sta­tis­tics, 144(4), 543–559. https://doi.org/10.1007/BF03399265

Briel­mann, A. A., Buras, N. H., Salin­garos, N. A., & Tay­lor, R. P. (2022). What Hap­pens in Your Brain When You Walk Down the Street? Impli­ca­tions of Archi­tec­tur­al Pro­por­tions, Bio­phil­ia, and Frac­tal Geom­e­try for Urban Sci­ence. Urban Sci­ence, 6(1), Arti­cle 1. https://doi.org/10.3390/urbansci6010003

Fleis­chmann, M., Romice, O., & Por­ta, S. (2021). Mea­sur­ing urban form: Over­com­ing ter­mi­no­log­i­cal incon­sis­ten­cies for a quan­ti­ta­tive and com­pre­hen­sive mor­pho­log­ic analy­sis of cities. Envi­ron­ment and Plan­ning B: Urban Ana­lyt­ics and City Sci­ence, 48(8), 2133–2150. https://doi.org/10.1177/2399808320910444

Frontczak, M., & War­goc­ki, P. (2011). Lit­er­a­ture sur­vey on how dif­fer­ent fac­tors influ­ence human com­fort in indoor envi­ron­ments. Build­ing and Envi­ron­ment, 46(4), 922–937. https://doi.org/10.1016/j.buildenv.2010.10.021

Hull, R. B., & Stew­art, W. P. (1995). The Land­scape Encoun­tered and Expe­ri­enced While Hik­ing. Envi­ron­ment and Behav­ior, 27(3), 404–426. https://doi.org/10.1177/0013916595273007

Rosen, S. (1974). Hedo­nic Prices and Implic­it Mar­kets: Prod­uct Dif­fer­en­ti­a­tion in Pure Com­pe­ti­tion. Jour­nal of Polit­i­cal Econ­o­my, 82(1), 34–55. JSTOR.Turan, I., Chegut, A., Fink, D., & Rein­hart, C. (2021). Devel­op­ment of View Analy­sis Met­rics and Their Finan­cial Impacts on Office Rents. SSRN Elec­tron­ic Jour­nal. https://doi.org/10.2139/ssrn.3784759

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