[et_pb_section fb_built=”1″ _builder_version=”4.16″ custom_padding=”0px|0px|0px|0px” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ custom_padding=”0px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.27.0″ custom_padding=”||5%||false|false” inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n
L’<\/span>artificial intelligence<\/strong> (AI) has established itself as a force to be reckoned with in many sectors, bringing a clear return on investment. However, its application in strategic areas, such as real estate decision-making, is less obvious. Find out in this article how the<\/span>AI<\/strong> can play a crucial role in the <\/span>real estate sector<\/strong> by focusing on a specific use case: projecting the <\/span>projecting the CSR trajectory of a real estate portfolio<\/strong>.<\/span><\/p>\n In the operational field,artificial intelligence<\/strong> has made a name for itself in many areas, thanks to its obvious return on investment. Let’s take the example of document classification: let’s consider a supervised trained classification model that we compare to a collaborator. The return on investment over 5 years will be the cost per employee to carry out all these actions over 5 years, minus the time it takes to set up the end-to-end model (data, development and industrialization) and to maintain it.<\/span><\/p>\n When we look at the functional domain, more specifically at supporting strategic<\/strong> decision-making, the profitability of implementing artificial intelligence<\/strong> is less natural.<\/span><\/p>\n So, in this article, I propose a strategic use case<\/strong> to give you an idea of what can be achieved in the functional field. The latter focuses on a property portfolio<\/strong>, with the aim of projecting its CSR trajectory<\/strong> (for decision-making purposes).<\/span><\/span><\/p>\n The resulting objectives are :<\/span><\/p>\n <\/span><\/p>\n The project was carried out in 6 distinct phases.<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ custom_padding=”||||false|false” inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ custom_padding=”||5%||false|false” inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n The American state of Washington has made this data available as open source data for its real estate portfolio<\/strong> (buildings owned by the state). The dataset covers two years: 2015 and 2016. It comprises 56 variables and almost 4,000 separate buildings. Thus, a line is uniquely identified by a building over a year…<\/span><\/p>\n This first step required a thorough understanding of the variables, creating a solid foundation for the rest of the project.<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ custom_padding=”||||false|false” inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ custom_padding=”||5%||false|false” inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n Our approach focused on a supervised regression model to predict energy consumption<\/strong> and greenhouse gas emissions<\/strong>. These 2 explanatory variables are part of our dataset<\/strong>, so we’re in the case of a supervised regression model, with a 2-dimensional output.<\/span><\/p>\n Here, we’ll assume that we’re at the start of 2016, so we have the explanatory variables for 2015 and 2016, but the variables to be explained are from 2015.<\/span><\/p>\n By considering these two explanatory variables, our model aimed toanticipate developments<\/strong> over the coming years<\/span>. <\/span> <\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ custom_padding=”||||false|false” inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ custom_padding=”||5%||false|false” inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n The actual data<\/strong>, while rich in information, required in-depth statistical analysis<\/strong> and careful cleaning to eliminate redundancies and variables of little interest to our study.<\/span><\/p>\n After a statistical study and a complete clean-up (the interdependence of certain variables or presenting little information), we obtain the following final dataset<\/strong>: 22 relevant variables for around 3,000 buildings.<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ custom_padding=”||||false|false” inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ custom_padding=”||5%||false|false” inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n For this section, only 2015 data<\/strong> are considered, as we need to have the variables to explain.<\/span><\/p>\n We have separated this dataset<\/strong> into 2 bases: a training base and a test base. This separation is stratified, with 80% of the lines in the training base and the remaining 20% in the test base.<\/span><\/p>\n A set of regression models<\/strong> was then defined, providing a complete approach, from the most naive (linear) to the most complex. Added to this is the implementation of a grid search coupled with 5-layer cross-validation based on 10 data separations<\/strong> (identical for all models). This enables all hyperparameter combinations to be tested, while limiting the uncertainty linked to the separation of data<\/strong> between test and training bases.<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ custom_padding=”||||false|false” inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ custom_padding=”||5%||false|false” inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n Numerous indicators can be used toassess the performance<\/strong> of these models, but the 2 indicators that have been chosen are :<\/span><\/p>\n Thus, the model with the best average balance (based on 10 iterations of 5-layer cross-validation) between maximizing R\u00b2 and minimizing mean squared error is the selected model.<\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ custom_padding=”||||false|false” inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n Finally, the model (trained and tested using 2015 data<\/strong> ) was applied to 2016 data. The results are presented in a PowerBI report<\/strong>. This report offers a detailed view by modality and a geographical perspective of the buildings, clearly presenting predictions of energy consumption<\/strong> andgreenhouse gas emissions<\/strong>.<\/span><\/p>\n It is composed of 2 tabs sensitive to different filters. The first tab is devoted to a detailed view by modality according to the variables selected. The second tab shows the geographical location of the various buildings.<\/span><\/p>\n On each of these tabs, we find within the header, 4 key indicators predicted for the year 2016: Total energy consumption<\/strong>,total greenhouse gas emissions<\/strong>, average energy consumption<\/strong> andaverage greenhouse gas emissions<\/strong>. <\/span>Below each of these indicators is the percentage increase or decrease compared with 2015.<\/span><\/p>\n As we can see in Appendix 1, the negative percentages of the 4 indicators in the heading for all buildings (comparing the figures predicted for 2016 with those for 2015) indicate that we will fall short of our target (consumption and emissions lower than the previous year). It is therefore possible toadjust the data for the explanatory variables<\/strong> in order toobtain a<\/strong> differentemissions and consumption trajectory<\/strong> and achieve these future targets.<\/span><\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/www.sqorus.com\/wp-content\/uploads\/2024\/08\/Consommation-Energetique.png” title_text=”Energy consumption” _builder_version=”4.27.0″ _module_preset=”default” custom_padding=”||5%||false|false” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.27.0″ custom_padding=”||||false|false” inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.0″ custom_padding=”||5%||false|false” inline_fonts=”Roboto” global_colors_info=”{}”]<\/p>\n This<\/span> POC (proof of concept), beyond a simple demonstration of feasibility, paves the way for a new approach to strategic decision-making in the real estate sector<\/strong>. However, we recognize that the presentation of results, while technical, must be accessible to a diverse audience. We’re committed to making this powerful technology simpler and more straightforward, demonstrating that data science<\/strong> can be understood and applied by everyone, regardless of their level of familiarity with the field.<\/span><\/p>\n If you’d also like to leverage the benefits ofartificial intelligence<\/strong> for CSR projections<\/strong> or any other topic, we can help you do just that. [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row custom_padding_last_edited=”on|desktop” _builder_version=”4.27.0″ background_color=”#2029a0″ background_image=”https:\/\/www.sqorus.com\/wp-content\/uploads\/2023\/12\/Bandeau-LB-Gouvernance-IT.png” background_size=”contain” background_position=”center_right” width=”100%” width_tablet=”90%” width_phone=”90%” width_last_edited=”on|phone” custom_margin=”||3%||false|true” custom_padding=”50px|400px|50px|55px|true|false” custom_padding_tablet=”50px|55px|50px|55px|true|true” custom_padding_phone=”40px|55px|40px|55px|true|true” background_last_edited=”on|phone” background_enable_image_tablet=”off” border_radii=”on|23px|23px|23px|23px” global_module=”25053″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text module_class=”Titre_40 Titre_blanc” _builder_version=”4.25.1″ _module_preset=”default” header_2_font_size=”30px” 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global_colors_info=”{}”]<\/p>\n Contact<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Titre – H2″ module_class=”Titre_40″ _builder_version=”4.25.0″ _module_preset=”default” text_orientation=”center” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text admin_label=”Texte” _builder_version=”4.25.1″ _module_preset=”default” text_text_color=”#d2d4ec” text_font_size=”21px” text_line_height=”35px” text_orientation=”center” global_colors_info=”{}”]<\/p>\n Contact us today and find out how we can work together to make your company’s digital future a reality.<\/p>\n [\/et_pb_text][et_pb_button button_url=”https:\/\/www.sqorus.com\/en\/contact\/” button_text=”Contact SQORUS” button_alignment=”center” module_class=”Bouton fond_orange” _builder_version=”4.25.2″ _module_preset=”default” custom_margin=”30px||||false|false” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" Discover how AI is revolutionizing real estate decision-making by projecting the CSR trajectory of a property portfolio.<\/p>\n","protected":false},"author":53,"featured_media":41358,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","content-type":"","_ayudawp_aiss_exclude":false,"footnotes":""},"categories":[91,92,559],"tags":[],"metiers":[225],"sujet":[546,218],"class_list":["post-41352","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-csr","category-data-analytics-en","category-intelligence-artificielle","metiers-it-en","sujet-csr","sujet-data-ia-en"],"acf":[],"yoast_head":"\n\n
1. Data selection, definition and extraction<\/h2>\n
2. Expression of hypotheses<\/h2>\n
3. Exploratory analysis<\/h2>\n
4. Modeling<\/h2>\n
5. Evaluation<\/h2>\n
\n
6. Application and results<\/h2>\n
Conclusion of our strategic CSR projection for a property portfolio<\/h2>\n
\nThe encouraging results point to the possibility of adjusting explanatory variables to meet future environmental targets. <\/span><\/span><\/p>\n
\nContact us today to discuss how SQORUS can guide you in integrating these innovative solutions into your strategy. <\/span><\/p>\nAll about IT project governance<\/h2>\n
Also in our “IT project governance” issue <\/span><\/h3>\n
\n
A project? A request?A question?<\/h2>\n