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Pet City 2 - Decorate & Design

Pet City 2 - Decorate & Design

Val Dsouza

Games免费v1.15.7
App Store
评分

4.9

3,939 条评分

星级

★★★★★

最近更新

2026年4月8日

发布日期

2020年3月4日

更新内容

v1.15.7

- Technical improvement for greater stability

应用信息

开发者
Val Dsouza
分类
Games
价格
免费
版本
1.15.7
App ID
1455225381

简介

Relax with adorable virtual pets, stylish dress up, and beautiful home decorating and design made for adults. * RELAX! Spend hours decorating beautiful rooms and enjoying your petís lovable personality. * BE NURTURING! Adopt and customize a cute virtual pet. Feed and care for your pet, and it will love you back. * DECORATE ROOMS! Discover beautifully drawn furniture and home dÈcor items, with pet-friendly collections released EVERY WEEK. * DRESS UP YOUR PET! Choose from super cute and stylish outfits, from magical fairy looks to rock star fashion. * MAKE NEW FRIENDS! Join a social network of players who love pets, decorating, design, and sharing ideas. * BE HELPFUL! Visit neighbor homes, send and receive gifts, and help each other with decorating inspiration. * JOIN TRADING GROUPS! Trade with other players to collect rare and highly sought-after dÈcor and dress up items. WHY IS PET CITY 2 SO ADDICTIVE? -- Your petís charming personality and sweet animations -- Weekly decor and fashion releases, every Monday. -- Trading is a huge part of the game -- A friendly community of wonderfully helpful players -- Advanced home decorating and design tools -- Lots of pet customization options to create the cutest pet your way -- Fun social interactions when visiting neighbor pets -- Animal lovers will love how your pet seems to have a life and personality of its own -- A huge variety of furniture and dÈcor items to decorate and create unique home designs -- Dress up items include modern fashion, retro fashion, holiday styles, formal wear, winter outfits, spring looks, and much more Join Pet City 2 today!

下载量预测

专业 · 预览

预估总下载量

197K131K394K
保守估计乐观估计

2K

低 / 月

3K

预估 / 月

5K

高 / 月

基于3,939 条评分
假设评分率2.0%
应用年龄75 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。