DVF statistics

Data statistics [DVF](https://www.data.gouv.fr/en/datasets/demandes-de-valeurs-foncieres/), available on [explore.data.gouv.fr/immobilier](https://explore.data.gouv.fr/immobilier). The files contain the number of sales, the average and the median of prices per m2. - Total DVF statistics: statistics by geographical scale, over the 10 semesters available. - Monthly DVF statistics: statistics by geographical scale and by month. ## Description of treatment The [code](https://github.com/etalab/datagouvfr_data_pipelines/tree/main/data_processing/dvf) allows statistics to be generated from the [data of land value requests](https://files.data.gouv.fr/geo-dvf/latest/csv/), aggregated at different scales, and their evolution over time (monthly). The following indicators have been calculated on a monthly basis and over the entire period available (10 semesters): * number of mutations * average prices per m2 * median of prices per m2 * Breakdown of sales prices by tranches for each type of property from: * houses * apartments * houses + apartments * commercial premises and for each scale from: * nation * Department * EPCI * municipality * Cadastral section The source data contain the following types of mutations: sale, sale in the future state of completion, sale of building land, tendering, expropriation and exchange. We have chosen to keep only sales, sales in the future state of completion and auctions for statistics*. In addition, for the sake of simplicity, we have chosen to keep only mutations that concern a single asset (excluding dependency)*. Our path is as follows: 1. for a transfer that would include assets of several types (e.g. a house + a commercial premises), it is not possible to reconstitute the share of the land value allocated to each of the assets included. 2. for a transfer that would include several assets of the same type (e.g. X apartments), the total value of the transfer is not necessarily equal to X times the value of an apartment, especially in the case where the assets are very different (area, work to be carried out, floor, etc.). We had initially kept these goods by calculating the price per m2 of the mutation by considering the goods of the mutation as a single good of an area to the sum of the surfaces of the goods, but this method, which ultimately concerned only a marginal quantity of goods, did not convince us for the final version. The price per m2 is then calculated by dividing the land value of the change by the surface area of the building of the property concerned. We finally exclude mutations for which we could not calculate the price per m2, as well as those whose price per m2 is more than € 100k (arbitrary choice)*. We have not incorporated any other outlier restrictions in order to maintain fidelity to the original data and to report potential anomalies. Displaying the median on the site reduces the impact of outliers on color scales. _*: The mentioned filters are applied for the calculation of statistics, but all mutations of the source files are well displayed on the application at the plot level.## DVF数据集统计说明 土地价值申请统计数据（DVF, Demandes de Valeurs Foncières）可从[explore.data.gouv.fr/immobilier](https://explore.data.gouv.fr/immobilier)获取。该数据集文件包含交易宗数、每平方米均价及每平方米价格中位数等统计指标。 - 全量DVF统计：基于现有10个学期的时间跨度，按地理尺度划分的统计结果 - 月度DVF统计：按地理尺度及月份划分的统计结果 ## 数据处理说明 本[代码仓库](https://github.com/etalab/datagouvfr_data_pipelines/tree/main/data_processing/dvf)可基于[土地价值申请原始数据](https://files.data.gouv.fr/geo-dvf/latest/csv/)生成不同聚合尺度下的统计指标，并展现其月度时间演化趋势。本次已基于月度维度及全量可用周期（10个学期）计算得到以下统计指标： * 交易宗数 * 每平方米均价 * 每平方米价格中位数 * 房价分段分布情况 统计覆盖以下物业类型：住宅、公寓、住宅+公寓组合、商业用房；同时覆盖以下地理尺度：国家级、省级行政区、跨市镇合作公共机构（EPCI）、市镇、地籍分区。 原始数据涵盖以下交易类型：现房买卖、期房买卖、建筑用地买卖、招标出让、征收及产权交换。本次统计仅保留现房买卖、期房买卖及拍卖三类交易用于统计计算*。 此外，为简化统计逻辑，本次统计仅保留涉及单一物业资产的交易（不含附属物业）*。具体处理规则如下： 1. 若一笔交易包含多种类型的物业资产（如住宅+商业用房），无法拆分出各类资产对应的土地价值占比； 2. 若一笔交易包含多笔同类型物业资产（如X套公寓），交易总价值未必等于单套公寓价值乘以X，尤其当各资产在面积、修缮需求、楼层等属性存在显著差异时。此前我们曾尝试将此类交易视为单宗合并资产，以总交易面积分摊总交易价值来计算每平方米价格，但该方法仅覆盖极少量样本，最终版本未采纳此方案。 每平方米价格通过将单宗交易的土地价值除以对应物业的建筑面积计算得出。最终我们剔除了无法计算每平方米价格的交易，以及每平方米单价超过10万欧元的交易（该阈值为人工设定）*。为保留原始数据的真实性并呈现潜在异常值，未增设其他异常值剔除规则。平台展示价格中位数可降低异常值对色彩映射标尺的影响。 _*: 上述筛选规则仅应用于统计计算环节，原始数据中的全部交易均可在应用的可视化图层中完整展示。